Search

Objective: We investigated genetic parameters of calving ease (CE) using several animal models in Korean Holstein and searched for suitable models for routine evaluation of CE. Methods: Two phenotypic datasets of CE (DS5 and DS10) on first-parity Korean Holstein calves were prepared. DS5 and DS10 included at least 5 and 10 CE records per herd-year level and comprised 117,921 and 80,389 observations, respectively. The CE phenotypes ranged from 1 to 4, from a normal to extreme difficulty calving scale. The CE was defined as a trait of the calf. The BLUPF90+ software was used for (co)variances estimation through four animal models with a maternal effect (M1 to M4), where all models included effects of a fixed calf-sex, a fixed dam calving age (covariate), and one or more fixed contemporary group (CG) terms. The CG effects were different across models--a herd-year-season (M1, HYS), a herd-year and year-season (M2, HY+YS), a herd-year and season (M3, HY+S), and a herd and year-season (M4, H+YS). Results: Direct heritability (h2) estimates of CE ranged from 0.005 to 0.234 across models and datasets. Maternal h2 values were low (0.001 to 0.090). Genetic correlations between direct and maternal effects were strongly negative to lowly positive (-0.814 to 0.078), further emphasizing its importance in CE evaluation models. These genetic parameter estimates also indicate slower future selection progress of CE in Korean Holsteins. The M1 fitted many levels with fewer observations per level deriving unreliable parameters, and the M4 did not account for confounded herd and animal structures. The M2 and M3 were deemed more realistic for implementation, and they were better able to account for data structure issues (incompleteness and confounding) than other models. Conclusion: As the pioneering study to employ animal models in Korean Holstein CE evaluation, our findings hold significant potential for this breed's future and routine evaluation development. [ABSTRACT FROM AUTHOR]

Simple Summary: Environmental indices are commonly used for detecting heat stress in dairy cattle; however, most studies have only focused on the productivity traits of dairy cattle in response to heat stress and have not considered their health and reproductive characteristics simultaneously. In this study, we aimed to determine the effects of heat stress on the production and reproduction performances of Thai–Holstein dairy cattle and the impact of the genetics of dairy cattle on their heat tolerance. We observed that heat stress significantly reduced milk yield and negatively affected the milk fat-to-protein ratio. Additionally, conception rates declined under heat stress, highlighting the challenges of finding an effective genetic approach for hot and humid regions, including Thailand. Genetic analysis revealed differences in heat tolerance among cows, indicating that the genetic improvement approach used in this study is suitable for planning future genetic improvements in dairy cattle. Heat stress severely affects dairy cattle production and reproduction performances in tropical regions. Genetic selection to maintain adequate yield and reproductive performance while enhancing their ability to withstand heat is essential for improving the genetics of dairy cows. Therefore, in this study, we aimed to estimate genetic parameters affecting production and reproduction performances under heat stress conditions in dairy cattle and to investigate the threshold point of heat stress for milk yield (MY), milk fat-to-protein ratio (FPR), and conception rate (CR) in Thai–Holstein dairy cattle. The data included 168,124 records related to MY and milk FPR and 21,278 records of CR in Thai–Holstein dairy cattle, covering the period from 1990 to 2007. A multiple-trait threshold-linear random regression model based on a Bayesian approach via Gibbs sampling was used to estimate variance components, genetic parameters (heritability values, and genetic correlations), and decline rates for each studied trait. The threshold point of heat stress was identified as a temperature and humidity index (THI) of 76. At THI76, a decline was observed in the MY, milk FPR, and CR of Thai dairy cattle. The heritability estimates for MY, milk FPR and CR were 0.347 ± 0.032, 0.293 ± 0.021, and 0.032 ± 0.001, respectively. The genetic correlation between MY and milk FPR and MY and CR were −0.24 and −0.53, respectively, whereas those between milk FPR and heat tolerance as well as between CR and heat tolerance were −0.48 and −0.49, respectively. In addition, the decline rates in MY, milk FPR, and CR were found to be associated with a high percentage of Holstein genetics. In conclusion, the results obtained in this study reveal that the simultaneous consideration of the MY, milk FPR, CR, and heat tolerance traits of Thai–Holstein dairy cattle is possible. In addition, developing a genetic model that incorporates THI is essential for sustainably addressing heat stress problems. [ABSTRACT FROM AUTHOR]

Simple Summary: Improving the genetic traits influencing rooster semen quality and quantity is crucial for optimizing poultry production. Breeders can identify heritable traits that significantly influence semen characteristics by evaluating genetic parameters, ensuring that only the best roosters are selected for breeding purposes. The development of appropriate genetic models aids in accurately predicting these traits, thereby enhancing selection efficiency. Selection based on these models boosts fertility and hatchability and promotes the propagation of desirable traits across generations. This approach leads to healthier, more productive flocks, increased economic efficiency, and more sustainable poultry farming practices. Improving reproductive traits, particularly semen quality and quantity, is crucial for optimizing poultry production and addressing the current limitations in native chicken reproduction. The aim of this study was to develop a genetic model to estimate genetic parameters guiding the selection of individual Thai native roosters. Using data collected from 3475 records of 242 Thai native grandparent roosters aged 1–4 years, we evaluated semen traits (mass movement, semen volume, and sperm concentration) over 54 weeks. A random regression test–day model incorporating five covariance functions, including a linear spline function with four, five, six, and eight knots (SP4, SP5, SP6, and SP8) and second-order Legendre polynomial function (LG2), was used to estimate genetic parameters. The results showed that the SP8 model consistently outperformed the other models across all traits, with the lowest mean square error, highest coefficient of determination, and superior predictive ability. Heritability estimates for mass movement, semen volume, and sperm concentration ranged from 0.10 to 0.25, 0.22 to 0.25, and 0.11 to 0.24, respectively, indicating moderate genetic influence on these traits. Genetic correlations between semen volume and sperm concentration were highest in the SP8 model, highlighting a strong genetic association between these traits. The SP8 model also revealed a high genetic correlation between mass movement and semen volume, supporting the potential for selecting mass movement as a predictor of semen volume. In conclusion, this study highlights the effectiveness of random regression models with linear spline functions to evaluate the genetic parameters of semen traits in native Thai roosters. The SP8 model is a robust tool for breeders to enhance the reproductive performance of native Thai chickens, contributing to sustainable poultry production systems. [ABSTRACT FROM AUTHOR]

The leopard coral grouper (Plectropomus leopardus) belongs to the family Epinephelinae, and genus Plectropomus. Vibrio harveyi is the main pathogen that causes "rot disease" in leopard coral grouper, which is a major threat to the sustainable development of its aquaculture industry. The disease is highly prevalent from June to August and severely affects aquaculture. Therefore, developing disease-resistant strains is a necessity. However, currently, artificial breeding techniques for leopard coral groupers cannot establish a family lineage through one-on-one artificial insemination, making traditional breeding methods that depend on a clear pedigree difficult. Considering the successful breeding of disease-resistant fish species with or without a pedigree, genome selection breeding technology are vital for cultivating disease-resistant leopard coral groupers.In genetic selection, the genetic parameters of target traits are important reference factors for specifying breeding programs. To evaluate the genetic parameters of leopard coral grouper resistance to V. harveyi, we constructed a genome-relatedness matrix based on high-density single-nucleotide polymorphisms using four models (binary linear model [BLM], binary threshold model [BTM], longitudinal linear model [LLM], and linear threshold model [LTM]) to fit two disease-resistant phenotypes (test-day trait, TDS; Bivariate survival trait, TS), and used restricted maximum likelihood [REML] to estimate variance components. Our findings illustrated that the genetic heritability of leopard coral grouper resistance to V. harveyi ranged from 0.182 to 0.486, which belongs to the medium-to-high genetic heritability range. The additive genetic variance ranged from 0.071 to 0.262. The genetic heritability estimated by the linear model was 0.382 and 0.476, whereas that estimated by the threshold model was 0.182 and 0.207, respectively. These results suggest that leopard coral groupers resistance to V. harveyi can be improved through genetic breeding.Herein, the linear models (BLM and LLM) obtained higher genetic heritability estimates and more accurate genomic estimated breeding value (GEBV) predictions than the threshold models (BTM and LTM). However, despite the model used, the correlation coefficient between the GEBV rankings under the same phenotype definition > 0.9, indicating that their impact on the GEBV ranking was not significant. Compared to the cross-sectional models (BLM and BTM), numerous leopard coral grouper GEBVs were rearranged in the LLM results. There was a strong correlation between the LTM and phenotype (TS), indicating that LLM has an excellent prediction effect. Therefore, when breeding leopard coral groupers for V. harveyi-resistant traits, a LLM should be considered.The study observed that using longitudinal models (LLM and LTM) to estimate genetic heritability produced higher results than the cross-sectional models (BLM and BTM), which may be due to the death time explaining different components of fish disease resistance. In longitudinal models, the genetic component influenced by the time of death is effectively harnessed. However, in cross-sectional models, this effect is inadvertently subsumed within the residuals. Consistent with the genetic heritability findings, the longitudinal models produced more precise GEBVs compared to cross-sectional models. Our results suggest that TDS might offer a more accurate measure for assessing the resistance of leopard coral groupers to V. harveyi than the TS.Compared with the threshold models, linear models performed better in GEBV prediction, and higher genetic heritability estimates were obtained. Although, most previous studies on disease resistance traits have reported inconsistent genetic heritability estimates between threshold and linear models, some studies support these conclusions. This result may be due to differences in information processing between the different models, which leads to different results. In this study, the additive genetic variance obtained using threshold models (BTM and LTM) was 0.222–0.262, and additive genetic variance obtained using linear models (LLM and BLM) was 0.071–0.086. It is expected that the additive genetic variance obtained using threshold models was higher than that obtained using linear models. Furthermore, the residual variance resulting from fitting linear models was notably low. We posit that when threshold traits are erroneously treated as normally distributed data and linear models are employed for analysis, the residual variance may be underestimated. This underestimation is likely due to the model's underfitting, which consequently leads to an inflated heritability estimate for the linear model.This study aimed to estimate the genetic parameters of leopard coral grouper resistance to V. harveyi using infection test data of leopard coral groupers injected with V. harveyi and to construct an individual genotype relationship matrix based on single-nucleotide polymorphisms. The genetic heritability of leopard coral grouper resistance to V. harveyi was estimated to be between 0.182 and 0.486 by comparing different models and phenotype definitions. The linear (0.382 and 0.476) and threshold models (0.182 and 0.207) were used to estimate genetic heritability. The estimated genetic heritability was within the medium genetic heritability range. Our findings were used to improve the target traits of leopard coral groupers, specifically their resistance to V. harveyi. This study supplements the genetic parameter estimation of leopard coral grouper resistance to V. harveyi and provides a reference for selecting V. harveyi-resistant leopard coral groupers for breeding.

Simple Summary: Dumeng sheep is a novel, specialized meat sheep breed created through crossbreeding innovation, cross-fixation, and herd expansion, using Dorper sheep as the father and Mongolian sheep as the mother. Given the critical role of early growth and development stages in determining the meat production potential of livestock animals, estimating the genetic parameters of early growth traits is crucial for the genetic advancement of meat sheep populations. This study aimed to investigate the genetic and non-genetic factors that affect the nine early growth traits of Dumeng sheep, as well as to estimate the variance components and genetic parameters associated with these traits. It was found that the growth traits of Dumeng sheep could be improved indirectly by selecting for weaning weight. This study aimed to estimate the genetic and non-genetic factors that affect the nine early growth traits of Dumeng sheep, as well as to estimate the variance components and genetic parameters associated with these traits. A dataset containing detailed information on 17,896 preweaning trait records of 4474 lambs was collected. In addition, 5015 postweaning trait records of 1003 lambs were documented. The effects of recipient dam age, sex, year, season, and herd on the early growth traits were assessed using the general linear model procedure of the statistical analysis system, revealing different levels of significance across different traits. To determine the most suitable model for estimating the genetic parameters, the likelihood ratio (LR) test was employed, fitting six animal models that either excluded or included maternal genetic and maternal permanent environmental effects within the average information restricted maximum likelihood (AIREML) framework using WOMBAT software (Version: 23/11/23). The model incorporating direct additive genetic effects, maternal genetic effects, and maternal permanent environment effects as random effects (model 6) provided the best fit for birth weight (BW) estimation. In contrast, the model combining direct additive genetic effects and maternal permanent environment effects as random effects (model 2) demonstrated a superior fit for estimating the genetic parameters of weaning weight (WW), average daily gain weight from birth to weaning (ADG1), and Kleiber ratio from birth to weaning (KR1). With regard to the genetic parameters of body weight at 6 months of age (6MW), average daily gain weight from weaning to 6 months (ADG2), average daily gain weight from birth to 6 months (ADG3), Kleiber ratio from weaning to 6 months (KR2), and Kleiber ratio from birth to 6 months (KR3), model 1, which incorporates only direct additive genetic effects, was identified as the optimal choice. With the optimal model, the heritability estimates ranged from 0.010 ± 0.033 for 6MW to 0.1837 ± 0.096 for KR3. The bivariate analysis method was employed to estimate the correlation between various traits using the most suitable model. The absolute values of genetic correlation coefficients among the traits spanned a range from 0.1460 to 0.9998, highlighting both weak and strong relationships among the studied traits. Specifically, the estimated genetic correlations between WW and ADG1, ADG3, KR1, and KR3 were 0.9859, 0.9953, 0.9911, and 0.9951, respectively, while the corresponding phenotypic correlations were 0.9752, 0.7836, 0.8262, and 0.5767. These findings identified that WW could serve as an effective selection criterion for enhancing early growth traits. [ABSTRACT FROM AUTHOR]

Simple Summary: This meta-analysis examines the impact of neutering on sheep production and quality, focusing on performance, carcass characteristics, and meat quality. It is observed that castrated sheep (wethers) exhibit enhanced daily weight gain and meat tenderness compared to intact rams. Furthermore, wethers display characteristics of a slenderer carcass with potentially elevated muscle content. By utilizing gene expression analysis, the research sheds light on genes associated with metabolic pathways and fat metabolism, indicating their involvement in fat formation. These results advocate for the practice of castration in sheep farming to enhance growth and meat quality. Additionally, the identified alterations in gene expression offer valuable insights for further exploration of castration's influence on muscle development in sheep. Objective: The study aimed to investigate the effects of castration on performance, carcass characteristics, and meat quality in sheep, as well as explore the expression of key genes related to metabolic pathways and muscle growth following castration. Methods: A meta-analysis approach was utilized to analyze data from multiple studies to compare the performance, carcass characteristics, and meat quality of castrated sheep (wethers) with intact rams. Additionally, protein–protein interaction (PPI) networks, differential gene expression (DEG) interactions, Gene Ontology (GO) terms, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were examined to identify molecular mechanisms associated with fat metabolism and muscle development in sheep tails. Results: The analysis revealed that castrated sheep (wethers) exhibited improved average daily gain, increased tenderness, lower backfat thickness, and a tendency for greater loin muscle area compared to intact rams. This suggests that castration promotes faster growth and results in leaner carcasses with potentially higher muscle content. Furthermore, the identification of downregulated DEGs like ACLY, SLC27A2, and COL1A1 and upregulated DEGs such as HOXA9, PGM2L1, and ABAT provides insights into the molecular mechanisms underlying fat deposition and muscle development in sheep. Conclusions: The findings support the practice of castration in sheep production as it enhances growth performance, leads to leaner carcasses with higher muscle content, and improves meat tenderness. The identified changes in gene expression offer valuable insights for further research into understanding the impact of castration on muscle development and fat metabolism in sheep. This meta-analysis contributes to the knowledge of molecular mechanisms involved in fat deposition in sheep, opening avenues for future investigations in livestock fat metabolism research. [ABSTRACT FROM AUTHOR]

ABSTRACT: Grandparent roosters are crucial in poultry breeding programs and significantly influence future bird generations' genetic makeup and performance. However, these roosters face considerable challenges from heat stress, which can adversely affect their reproductive performance, semen quality, and overall health and welfare. Our study aimed to investigate the effects of heat stress on the genetics of semen characteristics, identify the appropriate temperature and humidity indices (THI), and determine the threshold point of heat stress to prevent thermal stress. We analyzed data from 3,895 records of 242 Thai native grandparent roosters in conjunction with the THI using 7 THI functions and the regression method. The threshold point of heat stress, genetic parameters, rate of decline of semen characteristics per level of THI, estimated breeding values and selection index values were analyzed using the multivariate test-day model in the AIREML and BLUPF90 programs. Based on the regression coefficient and statistical criteria of the lowest −2logL and AIC values, the results showed that a THI of 78 was considered the threshold point of heat stress. The estimated heritability values ranged from 0.023 to 0.032, 0.066 to 0.069, 0.047 to 0.057, and 0.022 to 0.024 for mass movement, semen volume, sperm concentration, and the semen index, respectively. The reduction rates of mass movement, semen volume, sperm concentration, and semen index at a THI of 78 were −0.009, −0.003, −0.170, and −0.083 per THI, respectively. The genetic correlations among the semen traits were moderately to strongly positive and ranged from 0.562 to 0.797. The genetic correlations between semen traits and heat stress were negative and ranged from −0.437 to −0.749. The permanent environmental correlations among the semen traits (0.648–0.929) were positive and greater than the genetic correlations. Permanent environmental correlations between semen traits and heat stress were negative and ranged from −0.539 to −0.773. The results of the selection indices showed that the higher the selection intensity was, the greater the degree to which the selection index corresponded to genetic progress. The recommendation for animal genetic selection is that the top 10% is appropriate because it seems most preferred. Therefore, using a multivariate test-day model and selection index for the high genetic potential of semen traits and heat tolerance in Thai native grandparent roosters makes it possible to achieve genetic assessment in a large population.

ABSTRACT: Mastitis is one of the most frequent and costly diseases affecting dairy cattle. Natural antibodies (immunoglobulins) and cyclophilin A (CyPA), the most abundant member of the family of peptidyl prolyl cis/trans isomerases, in milk may serve as indicators of mastitis resistance in dairy cattle. However, genetic information for CyPA is not available, and knowledge on the genetic and nongenetic relationships between these immune-related traits and somatic cell score (SCS) and milk yield in dairy cattle is sparse. Therefore, we aimed to comprehensively evaluate whether immune-related traits consisting of 5 Ig classes (IgG, IgG1, IgG2, IgA, and IgM) and CyPA in the test-day milk of Holstein cows can be used as genetic indicators of mastitis resistance by evaluating the genetic and nongenetic relationships with SCS in milk. The nongenetic factors affecting immune-related traits and the effects of these traits on SCS were evaluated. Furthermore, the genetic parameters of immune-related traits according to health status and genetic relationships under different SCS environments were estimated. All immune-related traits were significantly associated with SCS and directly proportional. Additionally, evaluation using a classification tree revealed that IgA, IgG2, and IgG were associated with SCS levels. Genetic factor analyses indicated that heritability estimates were low for CyPA (0.08) but moderate for IgG (0.37), IgA (0.44), and IgM (0.44), with positive genetic correlations among Ig (0.25–0.96). We also evaluated the differences in milk yield and SCS of cows between the low and high groups according to their sires' estimated breeding value for immune-related traits. In the high group, IgA had a significantly lower SCS in milk at 7 to 30 d compared with that in the low group. Furthermore, the Ig in milk had high positive genetic correlations between healthy and infected conditions (0.82–0.99), suggesting that Ig in milk under healthy conditions could interact with those under infected conditions, owing to the genetic ability based on the level of Ig in milk. Thus, Ig in milk are potential indicators for the genetic selection of mastitis resistance. However, because only the relationship between immune-related traits and SCS was investigated in this study, further study on the relationship between clinical mastitis and Ig in milk is needed before Ig can be used as an indicator of mastitis resistance.

ABSTRACT: The continuous increasing demand for egg quality and quantity, and the expanding market share have enabled the egg industry to achieve significant benefits through genetic improvement. This study aims to estimate the genetic parameters and explore selectable breeding traits in the purebred Rhode Island Red (RIR) and White Leghorn (WL), which are 2 high-yielding layer breeds, and better understand their underlying genetic basis and accelerate genetic progress. The DMU software was utilized to analyze 12 egg quality traits, including egg length (EL), egg width (EW), egg shape index (ESI), egg weight (EWT), albumen height (AH), yolk color (YC), Haugh unit (HU), yolk weight (YW), albumen weight (AW), albumen-to-egg weight ratio (AWR), yolk-to-albumen ratio (YAR), and yolk-to-egg weight ratio (YWR). In RIR, the heritability of egg quality traits ranged from 0.196 to 0.427, while the repeatability ranged from 0.395 to 0.668. In WL, the heritability of egg quality traits ranged from 0.203 to 0.347, and the repeatability ranged from 0.424 to 0.656. In both RIR and WL, highly strong genetic correlations were observed between AW and EW, as well as between AW and EWT. The genetic correlations for AW and EW were 0.902 in RIR and 0.864 in WL, while the genetic correlations for AW and EWT were 0.981 in RIR and 0.960 in WL. The egg quality traits in both breeds showed moderate heritability, indicating great genetic potential for improvement through selective breeding. This can help breeders meet the increasingly diverse egg preferences of consumers through genetic selection. Additionally, there is a highly strong correlation between egg width/egg weight, and albumen weight in both breeds. In practical production, it is feasible to estimate albumen weight by measuring egg width and egg weight, which can simplify the method for measuring albumen weight. In conclusions, our finding provided valuable insights into the genetic architecture of egg quality traits in RIR and WL chickens. They help our understanding of the potential for genetic improvement of these traits through selective breeding programs.

Mineral composition in milk can affect its nutritional as well as physio-chemical properties of milk and is considered an important trait in the evaluation of milk quality. The composition and concentration of milk minerals could be altered with natural source of variation including nutrition and genetics. The effect of diet on milk minerals is well studied. However, genetic effects on the milk minerals have recently gained the attention. This review provides an overview of the genetic variation of milk minerals, and the genomic regions associated with mineral concentration in the milk are also discussed. The difference of milk minerals between breeds and the genetic parameters including heritability estimates and correlation among minerals indicates that milk minerals are under strong genetic control. Recently, the genome-wide association study (GWAS) has explored several regions associated with milk minerals and thus provides a new genetic source for improving the milk quality through genomics-assisted breeding. Hence, a combination of the qualitative and molecular approaches can be exploited to improving the nutritional quality of cattle milk in terms of its mineral composition. [ABSTRACT FROM AUTHOR]

Simple Summary: Pre-weaning mortality (PWM) is a major source of economic loss and welfare concern for goat producers. PWM is influenced by both genetic and non-genetic factors, and it varies across goat breeds and production systems. Despite its importance, there is limited research on risk factors and genetic analysis of PWM in goats. In this study, we aimed to identify fixed risk factors associated with PWM and estimate genetic parameters for PWM traits in a mixed population of purebred and crossbred Boer and Spanish goats. This study found that PWM is influenced by several factors, including birth weight, season, litter size, sex, dam age, breed, and heterosis. Furthermore, PWM has low to moderate heritability, and there are strong genetic correlations among different PWM traits defined at different pre-weaning stages. These findings provide valuable insights for goat producers to optimize their herd management and breeding programs to reduce PWM. The objectives of this study were to evaluate fixed risk factors associated with PWM and to estimate genetic parameters for PWM. A total of 927 birth records from a mixed population of purebred and crossbred Boer and Spanish goats born between 2016 and 2023 at the International Goat Research Center (IGRC) were used for this study. Four binary traits were studied: D0–3 (death within 3 days after birth), D4–60 (death between 4 and 60 days), D61–90 (death between 61 and 90 days), and D0–90 (death within 90 days). Logistic regression models were used to evaluate the risk factors associated with PWM traits. Bayesian threshold models and Gibbs sampling were used to estimate the genetic parameters. Birth weight, season, litter size, sex, dam age, breed, and heterosis were found to be significantly associated with at least one of the PWM traits. Heritability estimates were 0.263, 0.124, 0.080, and 0.207, for D0–3, D4–60, D61–90, and D0–90, respectively. The genetic correlations between the studied traits ranged from 0.892 (D0–3 and D0–90) to 0.999 (D0–3 and D61–90). These results suggest that PWM in goats is influenced by both non-genetic and genetic factors and can be reduced by management, genetic selection, and crossbreeding approaches. [ABSTRACT FROM AUTHOR]

Mastitis is one of the most frequent and costly diseases affecting dairy cattle. Natural antibodies (immunoglobulins) and cyclophilin A (CyPA), the most abundant member of the family of peptidyl prolyl cis / trans isomerases, in milk may serve as indicators of mastitis resistance in dairy cattle. However, genetic information for CyPA is not available, and knowledge on the genetic and nongenetic relationships between these immune-related traits and somatic cell score (SCS) and milk yield in dairy cattle is sparse. Therefore, we aimed to comprehensively evaluate whether immune-related traits consisting of 5 Ig classes (IgG, IgG 1 , IgG 2 , IgA, and IgM) and CyPA in the test-day milk of Holstein cows can be used as genetic indicators of mastitis resistance by evaluating the genetic and nongenetic relationships with SCS in milk. The nongenetic factors affecting immune-related traits and the effects of these traits on SCS were evaluated. Furthermore, the genetic parameters of immune-related traits according to health status and genetic relationships under different SCS environments were estimated. All immune-related traits were significantly associated with SCS and directly proportional. Additionally, evaluation using a classification tree revealed that IgA, IgG 2 , and IgG were associated with SCS levels. Genetic factor analyses indicated that heritability estimates were low for CyPA (0.08) but moderate for IgG (0.37), IgA (0.44), and IgM (0.44), with positive genetic correlations among Ig (0.25–0.96). We also evaluated the differences in milk yield and SCS of cows between the low and high groups according to their sires' estimated breeding value for immune-related traits. In the high group, IgA had a significantly lower SCS in milk at 7 to 30 d compared with that in the low group. Furthermore, the Ig in milk had high positive genetic correlations between healthy and infected conditions (0.82–0.99), suggesting that Ig in milk under healthy conditions could interact with those under infected conditions, owing to the genetic ability based on the level of Ig in milk. Thus, Ig in milk are potential indicators for the genetic selection of mastitis resistance. However, because only the relationship between immune-related traits and SCS was investigated in this study, further study on the relationship between clinical mastitis and Ig in milk is needed before Ig can be used as an indicator of mastitis resistance. [ABSTRACT FROM AUTHOR]

ABSTRACT: This study aimed to estimate genetic parameters for feeding behavior (FB) traits and to assess their genetic relationship with performance traits in group-housed broilers. In total, 99,472,151 visits were recorded for 95,711 birds between 2017 and 2022 using electronic feeders. The visits were first clustered into 2,667,617 daily observations for ten FB traits: daily feed intake (DFI), daily number of visits (NVIS), time spent at the feeders (TSF), number of visited feeders (NVF), visiting activity interval (VAI), feeding rate (FR), daily number of meals (NMEAL), average intake per meal (INTMEAL), number of visits per meal (VISMEAL) and interval between meals (MEALIVL). All FB traits were then considered as the average per bird across the feeding test period. Three growth traits (body weight at the start – SBW and at the end of the feeding test – FBW, and weight gain over the test period – BWG), and 2 feed efficiency (FE) traits (Feed Conversion Rate - FCR and Residual Feed Intake - RFI) were also recorded. The (co)variance components were estimated using multitrait animal mixed models. For growth and FE, the heritability (h2) estimates were moderate, ranging from 0.20 ± 0.01 (BWG) to 0.32 ± 0.02 (RFI). Overall, the h2 estimates for FB traits were higher than for productive traits, ranging from 0.31 ± 0.01 (DFI) to 0.56 ± 0.02 (TSF). DFI presented high genetic correlations (0.53–0.86) with all performance traits. Conversely, the remaining FB traits presented null to moderate genetic correlations with these traits, ranging from –0.38 to 0.42 for growth traits and between –0.14 and 0.25 for FE traits. Genetic selection for favorable feeding behavior is expected to exhibit a fast genetic response. The results suggest that it is possible to consider different feeding strategies without compromising the genetic progress of FE. Conversely, breeding strategies prioritizing a higher bird activity might result in lighter broiler lines in the long term, given the negative genetic correlations between visit-related traits (NV, NVF, and NMEAL) and growth traits (SBW and FBW).

Utilizing image-based computer vision techniques, many high-throughput phenotyping methods have been employed to capture intricate growth trait characteristics, offering reliable estimates of phenotypic traits crucial for breeding programs. In this study, we explored the application of partial differential equation (PDE)-based level set approaches to introduce image-based body area percentage (IBAP) as a novel growth trait in Plectropomus leopardus, as a substitution for the traditional growth trait body weight (BW). Assessing the genetic parameters essential for robust growth trait improvement in P. leopardus breeding programs, we estimated SNP-based heritability for IBAP and BW using a comprehensive set of SNPs (673,039 SNPs with MAF >2%). Results revealed heritability estimates of 0.515 (S.E. 0.06) for IBAP and 0.542 (S.E. 0.06) for BW. Moreover, strong phenotypic and genetic correlations of 0.812 (S.E. 0.001) and 0.903 (S.E 0.021) between IBAP and BW, respectively, underscored the potential for IBAP as a surrogate trait of BW for the genetic improvement of P. leopardus. We established a linear regression model of IBAP and BW (y ​= ​−730 ​+ ​1700×, (R2 ​= ​0.71)), after rigorous assessments of linearity, normality, and homoscedasticity, to confirm model fit. Evaluation of breeding value prediction accuracies using two linear models (rr-GBLUP and Bayes B) and a non-linear (RKHS) model demonstrated the superior performance of RKHS across IBAP and BW. Exploring the impact of varied marker densities for SNP selection on genomic prediction accuracy for IBAP and BW demonstrated a threshold of 10,000 SNPs for maximal model accuracy. These findings provide essential reference information and methodological groundwork for leveraging image-based traits in P. leopardus breeding endeavors, facilitating more efficient and precise genetic improvement programs.

The dataset focuses on evaluating the performance of 17 sweet potato varieties (G) released by the Bangladesh Agricultural Research Institute (BARI) in terms of storage root yield and stability across five locations (E) in Bangladesh—Gazipur, Bogura, Jamalpur, Jashore, and Chattogram. The result revealed that BARI Mistialu-12 exhibited the highest average storage root yield at 45.35 t/ha, closely followed by BARI Mistialu-16 at 44.64 t/ha. Conversely, BARI Mistialu-1 had the lowest mean yield of 25.99 t/ha. Among the locations, Bogura recorded the highest mean root yield at 37.05 t/ha, while Chattogram exhibited the lowest at 31.27 t/ha. A combined analysis of variance revealed the presence of variability in storage root yield attributed to the genotype-location (environment) interaction (GEI). To delve deeper into this interaction, additive and multiplicative interaction effect models (AMMI) along with a linear mixed model (LMM) were employed for further investigations to confirm the significant contribution of GEI variance to root yield. The LMM results showed genetic variance (%), heritability (%), selection accuracy (%), and GEI correlation coefficients of 52.27, 54, 94, and 30, respectively. The AMMI analysis indicated that the first two principal components accounted for 74.60 % of GEI, with 20.16 % attributed to it. Assessing significant Interaction Principal Component Analyses (IPCAs) through the Weighted Average of Absolute Scores (WAAS) indicated that BARI Mistialu-12 is the most stable genotype, followed by BARI Mistialu-16 and BARI Mistialu-8, all displaying above-average root yield. The mega-environment analysis associated the highest root production of BARI Mistialu-11 and BARI Mistialu-2 with the Jamalpur location, while Gazipur, Bogura, and Jashore were linked with the superior performance of BARI Mistialu-12 and BARI Mistialu-16 genotypes. These findings are crucial for future breeding programs and the rapidly growing sweet potato industry, given the stable high-yield potential across diverse agro-ecological conditions. However, it is imperative to repeat the study to ensure reliable outcomes.

Heat stress severely affects dairy cattle production and reproduction performances in tropical regions. Genetic selection to maintain adequate yield and reproductive performance while enhancing their ability to withstand heat is essential for improving the genetics of dairy cows. Therefore, in this study, we aimed to estimate genetic parameters affecting production and reproduction performances under heat stress conditions in dairy cattle and to investigate the threshold point of heat stress for milk yield (MY), milk fat-to-protein ratio (FPR), and conception rate (CR) in Thai–Holstein dairy cattle. The data included 168,124 records related to MY and milk FPR and 21,278 records of CR in Thai–Holstein dairy cattle, covering the period from 1990 to 2007. A multiple-trait threshold-linear random regression model based on a Bayesian approach via Gibbs sampling was used to estimate variance components, genetic parameters (heritability values, and genetic correlations), and decline rates for each studied trait. The threshold point of heat stress was identified as a temperature and humidity index (THI) of 76. At THI76, a decline was observed in the MY, milk FPR, and CR of Thai dairy cattle. The heritability estimates for MY, milk FPR and CR were 0.347 ± 0.032, 0.293 ± 0.021, and 0.032 ± 0.001, respectively. The genetic correlation between MY and milk FPR and MY and CR were −0.24 and −0.53, respectively, whereas those between milk FPR and heat tolerance as well as between CR and heat tolerance were −0.48 and −0.49, respectively. In addition, the decline rates in MY, milk FPR, and CR were found to be associated with a high percentage of Holstein genetics. In conclusion, the results obtained in this study reveal that the simultaneous consideration of the MY, milk FPR, CR, and heat tolerance traits of Thai–Holstein dairy cattle is possible. In addition, developing a genetic model that incorporates THI is essential for sustainably addressing heat stress problems.

Improving reproductive traits, particularly semen quality and quantity, is crucial for optimizing poultry production and addressing the current limitations in native chicken reproduction. The aim of this study was to develop a genetic model to estimate genetic parameters guiding the selection of individual Thai native roosters. Using data collected from 3475 records of 242 Thai native grandparent roosters aged 1–4 years, we evaluated semen traits (mass movement, semen volume, and sperm concentration) over 54 weeks. A random regression test–day model incorporating five covariance functions, including a linear spline function with four, five, six, and eight knots (SP4, SP5, SP6, and SP8) and second-order Legendre polynomial function (LG2), was used to estimate genetic parameters. The results showed that the SP8 model consistently outperformed the other models across all traits, with the lowest mean square error, highest coefficient of determination, and superior predictive ability. Heritability estimates for mass movement, semen volume, and sperm concentration ranged from 0.10 to 0.25, 0.22 to 0.25, and 0.11 to 0.24, respectively, indicating moderate genetic influence on these traits. Genetic correlations between semen volume and sperm concentration were highest in the SP8 model, highlighting a strong genetic association between these traits. The SP8 model also revealed a high genetic correlation between mass movement and semen volume, supporting the potential for selecting mass movement as a predictor of semen volume. In conclusion, this study highlights the effectiveness of random regression models with linear spline functions to evaluate the genetic parameters of semen traits in native Thai roosters. The SP8 model is a robust tool for breeders to enhance the reproductive performance of native Thai chickens, contributing to sustainable poultry production systems.

Simple Summary: The Litopenaeus vannamei shrimp is of paramount economic importance and has risen to prominence as a leading species in global aquaculture. Within its natural settings, Litopenaeus vannamei is subjected to a variety of environmental stressors that could negatively impact its survival, growth, and reproductive success. This study was designed to create specific lineages of Litopenaeus vannamei populations for resilience evaluation under stress conditions such as ammonia-N, pH, and salinity. Utilizing an exhaustive animal model that incorporates both genders, we performed a genetic parameter analysis to assess the heritability of stress tolerance traits in Litopenaeus vannamei. Concurrently, correlations among these traits were examined. Our findings lay solid theoretical groundwork for the selective breeding of Litopenaeus vannamei, providing valuable insights into enhancing varieties and improving economic yields. Furthermore, this research effort plays a crucial role in advancing the health and sustainable development of the aquaculture industry. Litopenaeus vannamei stands out globally in aquaculture for its fast growth, broad salt tolerance, disease resistance, and high protein levels. Selective breeding requires the precise estimation of the variance components and genetic parameters for important traits. This study formed lineages from 20 full sibling families of L. vannamei, with progenitors from Thailand and the USA. We then assessed the genetic resilience traits of juvenile shrimp from these families to high ammonia-N, high pH, and low salinity by performing a 96 h acute toxicity test. Mortality rates for the families under 96 h exposure to high ammonia-N, high pH, and low salinity were 19.52–92.22%, 23.33–92.22%, and 19.33–80.00%, respectively, showing significant variance in stress tolerance among families (p < 0.05). Survival heritability estimates, using threshold male and female models, were 0.44 ± 0.12 in high ammonia-N, 0.41 ± 0.12 in high pH, and 0.27 ± 0.08 in low salinity, respectively. Genetic correlations between growth and stress resistance traits varied from 0.0137 ± 0.2406 to 0.8327 ± 0.0781, and phenotypic correlations ranged from 0.0019 ± 0.0590 to 0.6959 ± 0.0107, indicating a low-to-high positive correlation significant at (p < 0.05). It was found that the survival rate of families No. 2 and No. 9 was higher under high ammonia-N and high pH stresses, while the survival rate of family No. 10 was higher under low salinity stress after comparing two selection criteria, the breeding values and phenotypic values. Thus, these three families are identified as potential breeding program candidates. Through the creation of a genetic parameter estimation model, the genetic variances across mating combinations for stress resistance traits were obtained and families with heightened stress resistance were identified, laying the groundwork for enhanced genetic selection of L. vannamei. [ABSTRACT FROM AUTHOR]

The present study was aimed at optimizing the selection strategy for enhancing reproductive efficiency and milk productivity of Alpine × Beetal crossbred goats. The data set included 2949 milk trait records across parities and 1389 milk records from first parity and corresponding reproductive traits. The traits included for analysis were 150‐day milk yield (150DMY), days in milk (DIM), peak yield (PY) and total milk yield (TMY). The litter size (LS) and litter weight (LW) were used for specifically formulating selection plan using indirect selection. The least squares mean for lactation traits during the first parity were 150DMY: 195.32 ± 2.09 kg, DIM: 236.42 ± 3.04 days, PY: 1.82 ± 0.02 kg, TMY: 269.62 ± 4.52 kg. Notably, Alpine × Beetal goats demonstrated genetic superiority pan India for milk productivity as compared to other native goat breeds. The least squares mean for 150DMY across all parities was 236 ± 3.13 kg. An animal model employing average information restricted maximum likelihood was used for (co)variance component estimation to get the genetic parameters. The analysis revealed total heritability estimates for 150DMY, DIM, PY and TMY as 0.18 ± 0.06, 0.04 ± 0.04, 0.12 ± 0.06 and 0.08 ± 0.05, respectively. Repeatability estimates for 150DMY, DIM, and TMY were 0.28 ± 0.04, 0.21 ± 0.03 and 0.37 ± 0.03, respectively. Bivariate analysis of 150DMY with reproductive traits revealed heritability for LS and LW as 0.05 ± 0.01 and 0.10 ± 0.01, respectively using Gibbs sampling. Strong and positive genetic correlations of 150DMY with other production and reproduction traits was observed, such as DIM (0.72), PY (0.98), TMY (0.88), LS (0.57) and LW (0.33). Moderate heritability and repeatability estimate of 150DMY, along with its positive correlation with production and reproductive traits suggested it as a suitable selection criterion for early selection and overall genetic progress of lactation traits. The genetic trend analysis showed an overall improvement in all these traits, with observed gain of 98.4 g per year for 150DMY, 0.04 days per year for DIM, 0.5 g per year for PY and 220.5 g per year for TMY. We observed that selecting based on 150DMY would lead to a favourable indirect improvement for LW as 79 g and LS 0.04 units per generation. We, therefore, recommend employing 150DMY as the single trait selection criteria to enhance both milk productivity and reproductive potential of Alpine × Beetal goats. [ABSTRACT FROM AUTHOR]

ABSTRACT: The aim of this study was to estimate genetic parameters and identify genomic regions associated with selected individual and groups of milk fatty acids (FA) predicted by milk mid-infrared spectrometry in Dual-Purpose Belgian Blue cows. The used data were 69,349 test-day records of milk yield, fat percentage, and protein percentage along with selected individual and groups FA of milk (g/dL milk) collected from 2007 to 2020 on 7,392 first-parity (40,903 test-day records), and 5,185 second-parity (28,446 test-day records) cows distributed in 104 herds in the Walloon Region of Belgium. Data of 28,466 SNPs, located on 29 Bos taurus autosomes (BTA), of 1,699 animals (639 males and 1,060 females) were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic best linear unbiased prediction approach. The proportion of genetic variance explained by each 25-SNP sliding window (with an average size of ~2 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. Average daily heritability estimated for the included milk FA traits ranged from 0.01 (C4:0) to 0.48 (C12:0) and 0.01 (C4:0) to 0.42 (C12:0) in the first and second parities, respectively. Genetic correlations found between milk yield and the studied individual milk FA, except for C18:0, C18:1 trans, C18:1 cis-9, were positive. The results showed that fat percentage and protein percentage were positively genetically correlated with all studied individual milk FA. Genome-wide association analyses identified 11 genomic regions distributed over 8 chromosomes [BTA1, BTA4, BTA10, BTA14 (4 regions), BTA19, BTA22, BTA24, and BTA26] associated with the studied FA traits, though those found on BTA14 partly overlapped. The genomic regions identified differed between parities and lactation stages. Although these differences in genomic regions detected may be due to the power of quantitative trait locus detection, it also suggests that candidate genes underlie the phenotypic expression of the studied traits may vary between parities and lactation stages. These findings increase our understanding about the genetic background of milk FA and can be used for the future implementation of genomic evaluation to improve milk FA profile in Dual-Purpose Belgian Blue cows.

This study aimed to estimate the genetic and non-genetic factors that affect the nine early growth traits of Dumeng sheep, as well as to estimate the variance components and genetic parameters associated with these traits. A dataset containing detailed information on 17,896 preweaning trait records of 4474 lambs was collected. In addition, 5015 postweaning trait records of 1003 lambs were documented. The effects of recipient dam age, sex, year, season, and herd on the early growth traits were assessed using the general linear model procedure of the statistical analysis system, revealing different levels of significance across different traits. To determine the most suitable model for estimating the genetic parameters, the likelihood ratio (LR) test was employed, fitting six animal models that either excluded or included maternal genetic and maternal permanent environmental effects within the average information restricted maximum likelihood (AIREML) framework using WOMBAT software (Version: 23/11/23). The model incorporating direct additive genetic effects, maternal genetic effects, and maternal permanent environment effects as random effects (model 6) provided the best fit for birth weight (BW) estimation. In contrast, the model combining direct additive genetic effects and maternal permanent environment effects as random effects (model 2) demonstrated a superior fit for estimating the genetic parameters of weaning weight (WW), average daily gain weight from birth to weaning (ADG1), and Kleiber ratio from birth to weaning (KR1). With regard to the genetic parameters of body weight at 6 months of age (6MW), average daily gain weight from weaning to 6 months (ADG2), average daily gain weight from birth to 6 months (ADG3), Kleiber ratio from weaning to 6 months (KR2), and Kleiber ratio from birth to 6 months (KR3), model 1, which incorporates only direct additive genetic effects, was identified as the optimal choice. With the optimal model, the heritability estimates ranged from 0.010 ± 0.033 for 6MW to 0.1837 ± 0.096 for KR3. The bivariate analysis method was employed to estimate the correlation between various traits using the most suitable model. The absolute values of genetic correlation coefficients among the traits spanned a range from 0.1460 to 0.9998, highlighting both weak and strong relationships among the studied traits. Specifically, the estimated genetic correlations between WW and ADG1, ADG3, KR1, and KR3 were 0.9859, 0.9953, 0.9911, and 0.9951, respectively, while the corresponding phenotypic correlations were 0.9752, 0.7836, 0.8262, and 0.5767. These findings identified that WW could serve as an effective selection criterion for enhancing early growth traits.

The aim of this study was to analyze suitable genetic models and selection indices to estimate the genetic parameters and breeding values of native Thai roosters. A total of 3475 records of seven semen traits (mass movement, semen pH, semen color, volume, sperm viability, sperm abnormalities, and sperm concentration) from 242 Thai native grandparent roosters were analyzed. Multiple-trait random regression test-day models with five covariance functions were used to analyze the variance components, genetic parameters, and breeding values. The selection index (SI) was calculated to determine the optimal genetic value for different selection percentages. The results showed that a multiple-trait random regression test-day model with a second-order Legendre polynomial function was the most appropriate genetic model for this population. The estimated heritability values were low to moderate, ranging from 0.110 to 0.112 (mass movement), 0.040 to 0.051 (semen pH), 0.092 to 0.097 (semen color), 0.220 to 0.225 (semen volume), 0.067 to 0.083 (sperm viability), 0.086 to 0.099 (sperm abnormalities), and 0.134 to 0.138 (sperm concentration). The repeatability values exceeded the heritability values and were within the range of 0.133 to 0.688. The genetic correlations among semen traits ranged from −0.332 to 0.677, and phenotypic correlations ranged from −0.260 to 0.460. When considering heritability and genetic correlation values, semen volume, sperm concentration, and mass movement were the top three priority semen traits calculated as selection indices. Finally, the top 10% of the selection index was recommended for creating the next generation. Our findings provide useful information on genetic parameters and an appropriate selection index of semen traits for selecting the genetics of individual Thai native grandparent roosters. The heritability estimates for semen traits reported here suggest an adequate response to selection through a genetic evaluation approach. Our results indicate that it is possible to select grandparent roosters with better reproductive performance.

Objective: The study aimed to investigate the effects of castration on performance, carcass characteristics, and meat quality in sheep, as well as explore the expression of key genes related to metabolic pathways and muscle growth following castration. Methods: A meta-analysis approach was utilized to analyze data from multiple studies to compare the performance, carcass characteristics, and meat quality of castrated sheep (wethers) with intact rams. Additionally, protein–protein interaction (PPI) networks, differential gene expression (DEG) interactions, Gene Ontology (GO) terms, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were examined to identify molecular mechanisms associated with fat metabolism and muscle development in sheep tails. Results: The analysis revealed that castrated sheep (wethers) exhibited improved average daily gain, increased tenderness, lower backfat thickness, and a tendency for greater loin muscle area compared to intact rams. This suggests that castration promotes faster growth and results in leaner carcasses with potentially higher muscle content. Furthermore, the identification of downregulated DEGs like ACLY, SLC27A2, and COL1A1 and upregulated DEGs such as HOXA9, PGM2L1, and ABAT provides insights into the molecular mechanisms underlying fat deposition and muscle development in sheep. Conclusions: The findings support the practice of castration in sheep production as it enhances growth performance, leads to leaner carcasses with higher muscle content, and improves meat tenderness. The identified changes in gene expression offer valuable insights for further research into understanding the impact of castration on muscle development and fat metabolism in sheep. This meta-analysis contributes to the knowledge of molecular mechanisms involved in fat deposition in sheep, opening avenues for future investigations in livestock fat metabolism research.

ABSTRACT: The aim of this study was to estimate genetic parameters for milk urea (MU) content in 3 main Danish dairy breeds. As a part of the Danish milk recording system, milk samples from cows on commercial farms were analyzed for MU concentration (mmol/L) and the percentages of fat and protein. There were 323,800 Danish Holstein, 70,634 Danish Jersey, and 27,870 Danish Red cows sampled with a total of 1,436,580, 368,251, and 133,922 test-day records per breed, respectively, included in the data set. Heritabilities for MU were low to moderate (0.22, 0.18, and 0.24 for the Holstein, Jersey, and Red breeds, respectively). The genetic correlation was close to zero between MU and milk yield in Jersey and Red, and −0.14 for Holstein. The genetic correlations between MU and fat and protein percentages, respectively, were positive for all 3 dairy breeds. Herd-test-day explained 51%, 54%, and 49% of the variation in MU in Holstein, Jersey, and Red, respectively. This indicates that MU levels in milk can be reduced by farm management. The current study shows that there are possibilities to influence MU by genetic selection as well as by farm management.

Objective Pork belly is a cut of meat with high worldwide demand. However, although the belly is comprised of multiple muscles and fat, unlike the loin muscle, research on their genetic parameters has yet to focus on a representative cut. To use swine breeding, it is necessary to estimate heritability against pork belly traits. Moreover, estimating genetic correlations is needed to identify genetic relationship among the traditional carcass and meat quality traits. This study sought to estimate the heritability of the carcass, belly, and their component traits, as well as the genetic correlations among them, to confirm whether these traits can be improved. Methods A total of 543 Yorkshire pigs (406 castrated males and 137 females) from 49 sires and 244 dam were used in this study. To estimate genetic parameters, a total of 12 traits such as lean meat production ability, meat quality and pork belly traits were chosen. The heritabilities were estimated by using genome-wide efficient mixed model association software. The statistical model was selected so that farm, carcass weight, sex, and slaughter season were fixed effects. In addition, its genetic parameters were calculated via MTG2 software. Results The heritability estimates for the 7th belly slice along the whole plate and its components were low to moderate (0.07±0.07 to 0.33±0.07). Moreover, the genetic correlations among the carcass and belly traits were moderate to high (0.28±0.20 to 0.99±0.31). Particularly, the rectus abdominis muscle exhibited a high absolute genetic correlation with the belly and meat quality (0.73±52 to 0.93±0.43). Conclusion A moderate to high correlation coefficient was obtained based on the genetic parameters. The belly could be genetically improved to contain a larger proportion of muscle regardless of lean meat production ability.

ABSTRACT: Considering the increasing challenges imposed by climate change and the need to improve animal welfare, breeding more resilient animals capable of better coping with environmental disturbances is of paramount importance. In dairy cattle, resilience can be evaluated by measuring the longitudinal occurrences of abnormal daily milk yield throughout lactation. Aiming to estimate genetic parameters for dairy cattle resilience, we collected 5,643,193 daily milk yield records on automatic milking systems (milking robots) and milking parlors across 21,350 lactations 1 to 3 of 11,787 North American Holstein cows. All cows were genotyped with 62,029 SNPs. After determining the best fitting models for each of the 3 lactations, daily milk yield residuals were used to derive 4 resilience indicators: weighted occurrence frequency of yield perturbations (wfPert), accumulated milk losses of yield perturbations (dPert), and log-transformed variance (LnVar) and lag-1 autocorrelation (rauto) of daily yield residuals. The indicator LnVar presented the highest heritability estimates (±standard error), ranging from 0.13 ± 0.01 in lactation 1 to 0.15 ± 0.02 in lactation 2; the other 3 indicators had relatively lower heritabilities across the 3 lactations (0.01–0.06). Based on bivariate analyses of each resilience indicator across lactations, stronger genetic correlations were observed between lactations 2 and 3 (0.88–0.96) than between lactations 1 and 2 or 3 (0.34–0.88) for dPert, LnVar, and rauto. For the pairwise comparisons of different resilience indicators within each lactation, dPert had the strongest genetic correlations with wfPert (0.64) and rauto (0.53) in lactation 1, whereas the correlations in lactations 2 and 3 were more variable and showed relatively high standard errors. The genetic correlation results indicated that different resilience indicators across lactations might capture additional biological mechanisms and should be considered as different traits in genetic evaluations. We also observed favorable genetic correlations of these resilience indicators with longevity and Net Merit index, but further biological validation of these resilience indicators is needed. In conclusion, this study provided genetic parameter estimates for different resilience indicators derived from daily milk yields across the first 3 lactations in Holstein cattle, which will be useful when potentially incorporating these traits in dairy cattle breeding schemes.

The study aimed to estimate genetic and phenotypic parameters for growth traits and Kleiber ratio in Dorper sheep breed. Data used in this study were collected over 12 years (2012–2023) at Debre Birhan Agricultural Research Center sheep research station in Ethiopia. Studied traits were body weights at birth (WT0), weaning (WT3), six month (WT6), and yearling (WT12) age; average daily gains from birth to weaning (ADG0-3), from weaning to six months (ADG3-6), from six months to yearling (ADG6-12); and Kleiber ratios from birth to weaning (KR1) and from weaning to six months (KR2). The (co)variance components were estimated with different animal models using Average Information Restricted Maximum Likelihood (AI-REML) procedure. The best-fitted model for each trait was determined using likelihood ratio tests. Phenotypic performance for WT3, WT6, WT12, ADG0-3 and ADG3-6 showed a decline trend at a rate of 0.216 kg, 0.794 kg, 0.671 kg, 2.601 g and 4.865 g over years respectively. However, WT3, WT6, WT12, ADG0-3 showed a positive genetic improvement trend at a rate of 0.029 kg, 0.043 kg, 0.049 kg and 0.257 g over years respectively. Year of birth had a significant effect (P

Estimation and prediction play a key role in breeding programs. Currently, phenotyping of complex traits such as nitrogen use efficiency (NUE) in wheat is still expensive, requires high-throughput technologies and is very time consuming compared to genotyping. Therefore, researchers are trying to predict phenotypes based on marker information. Genetic parameters such as population structure, genomic relationship matrix, marker density and sample size are major factors that increase the performance and accuracy of a model. However, they play an important role in adjusting the statistically significant false discovery rate (FDR) threshold in estimation. In parallel, there are many genetic hyper-parameters that are hidden and not represented in the given genomic selection (GS) model but have significant effects on the results, such as panel size, number of markers, minor allele frequency, number of call rates for each marker, number of cross validations and batch size in the training set of the genomic file. The main challenge is to ensure the reliability and accuracy of predicted breeding values (BVs) as results. Our study has confirmed the results of bias–variance tradeoff and adaptive prediction error for the ensemble-learning-based model STACK, which has the highest performance when estimating genetic parameters and hyper-parameters in a given GS model compared to other models. [ABSTRACT FROM AUTHOR]

The aim of this study was to estimate genetic parameters and identify genomic regions associated with selected individual and groups of milk fatty acids (FA) predicted by milk mid-infrared spectrometry in Dual-Purpose Belgian Blue cows. The used data were 69,349 test-day records of milk yield, fat percentage, and protein percentage along with selected individual and groups FA of milk (g/dL milk) collected from 2007 to 2020 on 7,392 first-parity (40,903 test-day records), and 5,185 second-parity (28,446 test-day records) cows distributed in 104 herds in the Walloon Region of Belgium. Data of 28,466 SNPs, located on 29 Bos taurus autosomes (BTA), of 1,699 animals (639 males and 1,060 females) were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic best linear unbiased prediction approach. The proportion of genetic variance explained by each 25-SNP sliding window (with an average size of ~2 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. Average daily heritability estimated for the included milk FA traits ranged from 0.01 (C4:0) to 0.48 (C12:0) and 0.01 (C4:0) to 0.42 (C12:0) in the first and second parities, respectively. Genetic correlations found between milk yield and the studied individual milk FA, except for C18:0, C18:1 trans , C18:1 cis -9, were positive. The results showed that fat percentage and protein percentage were positively genetically correlated with all studied individual milk FA. Genome-wide association analyses identified 11 genomic regions distributed over 8 chromosomes [BTA1, BTA4, BTA10, BTA14 (4 regions), BTA19, BTA22, BTA24, and BTA26] associated with the studied FA traits, though those found on BTA14 partly overlapped. The genomic regions identified differed between parities and lactation stages. Although these differences in genomic regions detected may be due to the power of quantitative trait locus detection, it also suggests that candidate genes underlie the phenotypic expression of the studied traits may vary between parities and lactation stages. These findings increase our understanding about the genetic background of milk FA and can be used for the future implementation of genomic evaluation to improve milk FA profile in Dual-Purpose Belgian Blue cows. [ABSTRACT FROM AUTHOR]

ABSTRACT: Sole hemorrhage and sole ulcers, referred to as sole lesions, are important causes of lameness in dairy cattle. The objective of this study was to estimate the genetic parameters of a novel trait reflecting how well cows recovered from sole lesions and the genetic correlation of this trait with overall susceptibility to sole lesions. A cohort of Holstein dairy cows was prospectively enrolled on 4 farms and assessed at 4 timepoints: before calving, immediately after calving, in early lactation, and in late lactation. At each timepoint, sole lesions were recorded at the claw level by veterinary surgeons and used to define 2 binary traits: (1) susceptibility to sole lesions—whether animals were affected with sole lesions at least once during the study or were unaffected at every assessment, and (2) sole lesion recovery—whether sole lesions healed between early and late lactation. Animals were genotyped and pedigree details extracted from the national database. Analyses were conducted with BLUPF90 software in a single-step framework; genetic parameters were estimated from animal threshold models using Gibbs sampling. The genetic correlation between both traits was approximated as the correlation between genomic estimated breeding values, adjusting for their reliabilities. A total of 2,025 animals were used to estimate the genetic parameters of sole lesion susceptibility; 44% of animals recorded a sole lesion at least once during the study period. The heritability of sole lesion susceptibility, on the liability scale, was 0.25 (95% highest density interval = 0.16–0.34). A total of 498 animals were used to estimate the genetic parameters of sole lesion recovery; 71% of animals had recovered between the early and late lactation assessments. The heritability of sole lesion recovery, on the liability scale, was 0.27 (95% highest density interval = 0.02–0.52). The approximate genetic correlation between each trait was −0.11 (95% confidence interval = −0.20 to −0.02). Our results indicate that recovery from sole lesions is heritable. If this finding is corroborated in further studies, it may be possible to use selective breeding to reduce the frequency of chronically lame cows. As sole lesion recovery appears to be weakly genetically related to sole lesion susceptibility, successful genetic improvement of sole lesion recovery would benefit from selection on this trait directly.

Horn flies are the most common summer pests of cattle, leading to substantial economic losses due to impaired production and decreased animal welfare. Genetic and non-genetic factors have been reported to influence cattle susceptibility to horn flies. However, the wide range of heritability estimates, and few identified genomic regions associated with cattle resistance to horn fly may be due to subjective assessment in collecting the fly load phenotype and the small size of the population assessed. Here, beef calves from the UF purebred Brahman (BRA) and Multibreed Angus-Brahman (MAB) populations were video recorded at weaning using a GoPro camera mounted on the top of the chute. Fly infestation was determined as the average fly count obtained from five longitudinal images of each animal using ImageJ software. A total of 748 crossbred and 415 purebred calves born from 2021 to 2023 were evaluated during summer (August or September). Analysis of variance (ANOVA) was performed within herd by fitting linear models that included collection date, age, skin color, coat color, and pre-weaning average daily gain (ADG) as fixed effects, and residual as a random effect. For MAB calves, breed groups (BG) determined based on the percentage of Brahman breed, with BG1 ranging from 0 to 19%, BG2 from 20 to 39%, BG3 from 40 to 59%, BG4 from 60 to 79%, BG5 from 80 to 100%, were also fit in the model. Fly count was transformed to a natural log value (log[count +1]) ensuring normally distribute residuals. As expected, darker colors were significantly more attractive to horn flies, while animals with greaterr percentage of Brahman were less susceptible than animals with greater percentage of Angus breed. Considerable variability in fly load was observed within purebred Brahmans, indicating that additional genetic factors, other than breed, influence cattle resistance to flies. Positive estimates were observed between horn fly load and average daily gain in both herds, which can be due to the fly load below the previously reported economic injury level of 200 flies (BRA: 28.78 ± 38.20, MAB: 19.31 ± 26.72). Variance components were estimated within herds by fitting the same aforementioned effects and a genomic-based relationship matrix (G) constructed with genotypes from the GeneSeek Genomic Profiler F250 SNP chip. The moderate heritability estimate demonstrates the feasibility of selecting cattle resistant to horn flies. Overall, our findings have the potential to contribute to reducing losses and costs in beef cattle operations, while reducing the impact on the environment of overuse of insecticides. [ABSTRACT FROM AUTHOR]

The objectives of this study were to evaluate fixed risk factors associated with PWM and to estimate genetic parameters for PWM. A total of 927 birth records from a mixed population of purebred and crossbred Boer and Spanish goats born between 2016 and 2023 at the International Goat Research Center (IGRC) were used for this study. Four binary traits were studied: D0–3 (death within 3 days after birth), D4–60 (death between 4 and 60 days), D61–90 (death between 61 and 90 days), and D0–90 (death within 90 days). Logistic regression models were used to evaluate the risk factors associated with PWM traits. Bayesian threshold models and Gibbs sampling were used to estimate the genetic parameters. Birth weight, season, litter size, sex, dam age, breed, and heterosis were found to be significantly associated with at least one of the PWM traits. Heritability estimates were 0.263, 0.124, 0.080, and 0.207, for D0–3, D4–60, D61–90, and D0–90, respectively. The genetic correlations between the studied traits ranged from 0.892 (D0–3 and D0–90) to 0.999 (D0–3 and D61–90). These results suggest that PWM in goats is influenced by both non-genetic and genetic factors and can be reduced by management, genetic selection, and crossbreeding approaches.

The collective economic and environmental interest of the whole dairy sheep sector is to reduce feed costs and the negative impact of milk production on the environment. Thus, this study focused on the characterisation and genetic selection potential of feed efficiency in the Lacaune breed. Estimates for feed efficiency in dairy ewes are limited, mainly due to a lack of individual feed intake measurements in the sheepfold or in the pasture. We estimated the genetic parameters for two approximated (not entirely based on individual data) feed efficiency traits (lactation feed conversion ratio (LFCR) and residual energy intake (REI)) and daily milk yield (DMY) at different stages of lactation and throughout lactation. The accuracy of the efficiency traits was first evaluated on samples from Lacaune dairy ewes that were monitored individually, especially for their feed intake. Then, feed efficiency estimation methods were applied on eight commercial farms corresponding to 4 680 Lacaune dairy ewes over two milk lactations (30 854 records). Animals were collectively (for a large part of feed intake) or individually (for milk performance and dynamics of body fat reserves) monitored at different lactation stages. The heritabilities of LFCR and REI were estimated over lactations at 0.10 ± 0.01 and 0.11 ± 0.01, respectively. High genetic correlations were observed between the two efficiency traits and milk production traits, with a genetic correlation between LFCR and DMY of 0.74 ± 0.04 and between REI and DMY of −0.79 ± 0.04. A strong influence of environmental factors such as farm, year of milk production and lactation stage affected the genetic link between REI and milk production traits. Efficiency values observed in early lactation when animals were bred in the sheepfold were less genetically correlated with values obtained later in lactation when animals were grass-fed. However, individual characterisation of feed efficiency remains difficult due to the collective feeding context in dairy ewe farms.

ABSTRACT: Egg products from indigenous chickens have growing market shares as consumers are pursuing differentiation in egg consumption. The genetic improvement in egg production performance of those breeds is crucial for increasing the economic profit. This study aimed to estimate genetic parameters for egg production and clutch-related traits in indigenous Beijing-You chickens for understanding the genetic architecture and exploring proper biological traits for selection. Data on traits including age at first egg (AFE), egg number (EN), average clutch length (ACL), maximum clutch length (MCL), number of clutches (NC) and pauses (NP), and average pause length (APL) were collected from 4 generations of purebred Beijing-You chickens based on the 43-wk and 66-wk of individual egg production record. The heritabilities, genetic and phenotypic correlations were analyzed by the DMU software with the restricted maximum likelihood method in a multivariate animal model. The results showed that the AFE of Beijing-You chickens was 174.45 d of age, and its heritability was as high as 0.62. The heritability was 0.26 for EN43 and 0.18 for EN66. The clutch traits including ACL, MCL, NC, and NP were moderate to high heritable (h2 = 0.15–0.39), but APL was very low heritable (h2 = 0.05). Genetic correlations were high between AFE and EN (rG(AFE, EN43) = −0.79, rG(AFE, EN66) = −0.39), whereas low between AFE and ACL (rG(AFE, ACL43) = −0.08, rG(AFE, ACL66) = 0.01) and MCL (rG(AFE, MCL) = −0.07). EN had higher correlations with ACL (rG(EN43, ACL43) = 0.59, rG(EN66, ACL66) = 0.40) than that with MCL (rG(EN43, MCL43) = 0.56, rG(EN66, MCL66) = 0.32). The heritability for ACL43 (h2 = 0.38) was higher than that for MCL43 (h2 = 0.33). ACL43 had a positive correlation with EN66 (rG(ACL43, EN66) = 0.62). These results indicated that the egg production of whole laying period could be improved by early selection for AFE and ACL at the same time in Beijing-You chickens.

The aim of this study was to estimate genetic parameters for milk urea (MU) content in 3 main Danish dairy breeds. As a part of the Danish milk recording system, milk samples from cows on commercial farms were analyzed for MU concentration (mmol/L) and the percentages of fat and protein. There were 323,800 Danish Holstein, 70,634 Danish Jersey, and 27,870 Danish Red cows sampled with a total of 1,436,580, 368,251, and 133,922 test-day records per breed, respectively, included in the data set. Heritabilities for MU were low to moderate (0.22, 0.18, and 0.24 for the Holstein, Jersey, and Red breeds, respectively). The genetic correlation was close to zero between MU and milk yield in Jersey and Red, and −0.14 for Holstein. The genetic correlations between MU and fat and protein percentages, respectively, were positive for all 3 dairy breeds. Herd-test-day explained 51%, 54%, and 49% of the variation in MU in Holstein, Jersey, and Red, respectively. This indicates that MU levels in milk can be reduced by farm management. The current study shows that there are possibilities to influence MU by genetic selection as well as by farm management. [ABSTRACT FROM AUTHOR]

The present study was carried out to estimate genetic and phenotypic parameters for growth rate and efficiency-related traits in Dorper crossbred sheep population. Data on body weight collected from 2012 to 2021 at Debre Birhan Agricultural Research Center, Amhara Regional State, Ethiopia, were used to estimate phenotypic and genetic parameters for daily gain from birth to weaning (DG0-3), daily gain from weaning to 6 months (DG3-6), and daily gain from 6 months to yearling (DG6-12) and corresponding Kleiber ratios (KR0-3, KR3-6, KR6-12), efficiency of growth (GE0-3, GE3-6, GE6-12), and relative growth rate (RG0-3, RG3-6, RG6-12). Genetic parameters were estimated by restricted maximum likelihood (REML) procedure fitting six different univariate animal models and the most appropriate model for each trait was determined by log-likelihood ratio test. Multivariate analysis was carried out to estimate correlations between traits. Year and season of birth had a significant effect (p<0.001) in all studied traits. Direct heritability estimates for DG0-3, DG3-6, DG6-12, KR0-3, KR3-6, KR6-12, GE0-3, GE3-6, GE6-12, GR0-3, GR3-6, and GR6-12 were 0.45±0.15, 0.04±0.06, 0.15±0.11, 0.30±0.08, 0.13±0.11, 0.14±0.12, 0.34±0.15, 0.39±0.17, 0.31±0.14, 0.25±0.08, 0.23±0.13, and 0.23±0.13 respectively. Genetic correlation estimates between DG3-6 and other traits were positive and high in magnitude to their respective growth phase (0.95, 0.86, and 0.91 for KR3-6, GE3-6, and GR3-6 respectively). As the Dorper crossbred sheep are reaches market weight at about 6 months of age, focusing on improving traits measured during weaning to 6 months of age is more feasible. Selection based on DG3-6 is recommended to improve efficiency-related traits. [ABSTRACT FROM AUTHOR]

Ningxiang pig is a breed renowned for its exceptional meat quality, but it possesses suboptimal carcass traits. To elucidate the genetic architecture of meat quality and carcass traits in Ningxiang pigs, we assessed heritability and executed a genome-wide association study (GWAS) concerning carcass length, backfat thickness, meat color parameters (L.LD, a.LD, b.LD), and pH at two postmortem intervals (45 min and 24 h) within a Ningxiang pig population. Heritability estimates ranged from moderate to high (0.30~0.80) for carcass traits and from low to high (0.11~0.48) for meat quality traits. We identified 21 significant SNPs, the majority of which were situated within previously documented QTL regions. Furthermore, the GRM4 gene emerged as a pleiotropic gene that correlated with carcass length and backfat thickness. The ADGRF1, FKBP5, and PRIM2 genes were associated with carcass length, while the NIPBL gene was linked to backfat thickness. These genes hold the potential for use in selective breeding programs targeting carcass traits in Ningxiang pigs. [ABSTRACT FROM AUTHOR]

Considering the increasing challenges imposed by climate change and the need to improve animal welfare, breeding more resilient animals capable of better coping with environmental disturbances is of paramount importance. In dairy cattle, resilience can be evaluated by measuring the longitudinal occurrences of abnormal daily milk yield throughout lactation. Aiming to estimate genetic parameters for dairy cattle resilience, we collected 5,643,193 daily milk yield records on automatic milking systems (milking robots) and milking parlors across 21,350 lactations 1 to 3 of 11,787 North American Holstein cows. All cows were genotyped with 62,029 SNPs. After determining the best fitting models for each of the 3 lactations, daily milk yield residuals were used to derive 4 resilience indicators: weighted occurrence frequency of yield perturbations (wfPert), accumulated milk losses of yield perturbations (dPert), and log-transformed variance (LnVar) and lag-1 autocorrelation (r auto) of daily yield residuals. The indicator LnVar presented the highest heritability estimates (±standard error), ranging from 0.13 ± 0.01 in lactation 1 to 0.15 ± 0.02 in lactation 2; the other 3 indicators had relatively lower heritabilities across the 3 lactations (0.01–0.06). Based on bivariate analyses of each resilience indicator across lactations, stronger genetic correlations were observed between lactations 2 and 3 (0.88–0.96) than between lactations 1 and 2 or 3 (0.34–0.88) for dPert, LnVar, and r auto. For the pairwise comparisons of different resilience indicators within each lactation, dPert had the strongest genetic correlations with wfPert (0.64) and r auto (0.53) in lactation 1, whereas the correlations in lactations 2 and 3 were more variable and showed relatively high standard errors. The genetic correlation results indicated that different resilience indicators across lactations might capture additional biological mechanisms and should be considered as different traits in genetic evaluations. We also observed favorable genetic correlations of these resilience indicators with longevity and Net Merit index, but further biological validation of these resilience indicators is needed. In conclusion, this study provided genetic parameter estimates for different resilience indicators derived from daily milk yields across the first 3 lactations in Holstein cattle, which will be useful when potentially incorporating these traits in dairy cattle breeding schemes. [ABSTRACT FROM AUTHOR]

The purpose of this study was to use nonlinear models (NLN)to characterize the growth pattern and estimate the heritability (h² ), the genetic (rg), and phenotypic(rp) correlation of the body weight (BW) in two distinct genotypes of chicken raising under Egyptian conditions. A data set of 500 chickens with pedigree information was gathered for this study. For this purpose, the BW was measured at hatching (BW0),4 weeks (BW4), 8 weeks (BW8), and 12 weeks (BW 12) of age. Three NLN models (Logistic, Gompertz, and Von Bertalanffy) were used. Using Wombat software, a multi-trait animal model with a restricted maximum likelihood procedure was used to estimate h², rg, and rpfor BW.The results showed that Golden Sabahi (GS) had a significantly higher weight than White Leghorn (WL). The growth curve parameters A (mature body weight), B (biological constant), and K (growth rate) were 3989.9, 0.7853, and 0.0624 for WL and 4332.6,0.7897, and 0.0642 for GS in the von Bertalanffy model, 2152.8, 3.8096, and 0.1322 for WL chickens and 2368, 3.8594, and 0.1350 for GS chickens in the Gompertz model, 1304.5, 19.0421, and 0.3382 for WL chickens and 1455.6, 19.6116, and 0.3411 for GS chickens in the logistic model. Three models represented the growth of the two breeds using goodness-of-fit metrics (R2, MSE, and AIC). Heritability estimates of BW at 0, 4, and 8 were higher in GS than WL, while the estimate of BW at 12 weeks of age was almost similar in the both strains GS and WL (0.1). Between BW0 and BW12, there were strong positive genetic and phenotypic correlations compared by the rest of growth traits. Based on the findings of this investigation, we recommend that the two strains can be utilized for selective breeding between the ages of 4 and 8 weeks to increase the overall genetic improvement of growth traits. [ABSTRACT FROM AUTHOR]

Bayesian and average information restricted maximum likelihood (AI-REML) approaches were used to estimate variance components and genetic parameters of Wood’s lactation curve parameters of milk production traits in three lactations of Iranian Holstein dairy cows. Wood’s function parameters (a, b, and c) of each lactation were initially estimated for individual cows using the NLIN procedure of the SAS programme, separately. The variance components of the parameters of each lactation were estimated by the AI-REML method using a single-trait animal model inairemlf90 programme. To do this with the Bayesian method by using the Gibbs sampling technique, the same animal model was used in the gibbs3f90 programme. The estimated heritability values by the AI-REML were 0.018, 0.00, and 0.019 for a parameter; 0.015, 0.007, and 0.02 for b parameter; and 0.23, 0.625, and 0.049 for c parameter in the 1st, 2nd, and 3rd lactations, respectively. The corresponding results from the Bayesian procedure (a posterior means) were 0.019, 0.01, and 0.035 for a parameter; 0.018, 0.019, and 0.043 for b parameter; and 0.024, 0.1, and 0.058 for c parameter, respectively. These obtained results reveal that the differences between the Bayesian and AI-REML methods in terms of estimation of heritability and variance components were small.

Objective The primary objective of this study was to determine the genetic parameters for reproductive traits among Large White pigs, including the following traits: total number born (TNB), number born alive (NBA), litter birth weight (LBW), average birth weight (ABW), gestation length (GL), age at first service (AFS) and age at first farrowing (AFF). Methods The dataset consisted of 19,036 reproductive records from 4,986 sows, and a multi-trait animal model was used to estimate genetic variance components of seven reproductive traits. Results The heritability estimates for these reproductive traits ranged from 0.09 to 0.26, with the highest heritability for GL and AFF, and the lowest heritability for NBA. The repeatabilities for TNB, NBA, LWB, ABW, and GL were ranged from 0.16 to 0.34. Genetic and phenotypic correlations ranged from −0.41 to 0.99, and −0.34 to 0.98, respectively. In particular, the correlations between TNB, NBA and LBW, between AFS and AFF, exhibited a strong positive correlation. Furthermore, for TNB, NBA, LBW, ABW, and GL, genetic correlations of the same trait between different parities were moderately to strongly correlated (0.32 to 0.97), and the correlations of adjacent parities were higher than those of nonadjacent parities. Conclusion All the results in the present study can be used as a basis for the genetic assessment of the target population. In the formulation of dam line selection index, AFS or AFF can be considered to combine with TNB in a multiple trait swine breeding value estimation system. Moreover, breeders are encouraged to increase the proportion of sows at parity 3–5 and reinforce the management of sows at parity 1 and parity ≥8.

ABSTRACT: This study aimed to estimate genetic parameters and identify genomic region(s) associated with selected cheese-making properties (CMP) in Dual-Purpose Belgian Blue (DPBB) cows. Edited data were 46,301 test-day records of milk yield, fat percentage, protein percentage, casein percentage, milk calcium content (CC), coagulation time (CT), curd firmness after 30 min from rennet addition (a30), and milk titratable acidity (MTA) collected from 2014 to 2020 on 4,077 first-parity (26,027 test-day records), and 3,258 second-parity DPBB cows (20,274 test-day records) distributed in 124 herds in the Walloon Region of Belgium. Data of 28,266 SNP, located on 29 Bos taurus autosomes (BTA) of 1,699 animals were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic BLUP approach. The proportion of the total additive genetic variance explained by windows of 25 consecutive SNPs (with an average size of ∼2 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. Heritability estimates for the included CMP ranged from 0.19 (CC) to 0.50 (MTA), and 0.24 (CC) to 0.41 (MTA) in the first and second parity, respectively. The genetic correlation estimated between CT and a30 varied from −0.61 to −0.41 and from −0.55 to −0.38 in the first and second lactations, respectively. Negative genetic correlations were found between CT and milk yield and composition, while those estimated between curd firmness and milk composition were positive. Genome-wide association analyses results identified 4 genomic regions (BTA1, BTA3, BTA7, and BTA11) associated with the considered CMP. The identified genomic regions showed contrasting results between parities and among the different stages of each parity. It suggests that different sets of candidate genes underlie the phenotypic expression of the considered CMP between parities and lactation stages of each parity. The findings of this study can be used for future implementation and use of genomic evaluation to improve the cheese-making traits in DPBB cows.

ABSTRACT: Pregnancy loss is recognized as one of the major factors contributing to poor reproductive performance in dairy cattle. Here, we performed a comprehensive genetic analysis of fetal loss, defined as a pregnancy loss that occurs after detection of a viable embryo around 42 d of gestation. The objectives of this study were to reveal (1) whether fetal loss is heritable and, hence, whether it will respond to selection, and (2) to what extent current fertility traits, such as daughter pregnancy rate, are associated with fetal loss. Data consisted of 59,308 confirmed pregnancy or fetal loss records distributed across nulliparous heifers and primiparous and multiparous cows. We defined fetal loss as a binary trait (yes vs. no) or as an ordinal trait (pregnancy maintenance, early fetal loss ≤150 d of gestation, and late fetal loss >150 d of gestation), and we assessed both linear and threshold models. Heritability estimates for fetal loss ranged from 1 to 18%, depending upon parity, trait definition, and statistical model used. Heritability estimates were greater for lactating cows than for nonlactating nulliparous heifers. Threshold models were able to capture more additive genetic variance and, thus, yielded higher heritability estimates than linear models. Notably, fetal loss traits were highly genetically correlated with each other but only weakly correlated with current fertility traits included in the national genetic evaluation. Overall, our study provides evidence that fetal loss is heritable enough to make genetic selection for reducing fetal loss and improving pregnancy maintenance feasible. In addition, our results suggest that fetal loss is largely independent from current traits used to select for cow fertility, and thus current breeding efforts have unfortunately little effect on reducing the incidence of fetal loss.

Sole hemorrhage and sole ulcers, referred to as sole lesions, are important causes of lameness in dairy cattle. The objective of this study was to estimate the genetic parameters of a novel trait reflecting how well cows recovered from sole lesions and the genetic correlation of this trait with overall susceptibility to sole lesions. A cohort of Holstein dairy cows was prospectively enrolled on 4 farms and assessed at 4 timepoints: before calving, immediately after calving, in early lactation, and in late lactation. At each timepoint, sole lesions were recorded at the claw level by veterinary surgeons and used to define 2 binary traits: (1) susceptibility to sole lesions—whether animals were affected with sole lesions at least once during the study or were unaffected at every assessment, and (2) sole lesion recovery—whether sole lesions healed between early and late lactation. Animals were genotyped and pedigree details extracted from the national database. Analyses were conducted with BLUPF90 software in a single-step framework; genetic parameters were estimated from animal threshold models using Gibbs sampling. The genetic correlation between both traits was approximated as the correlation between genomic estimated breeding values, adjusting for their reliabilities. A total of 2,025 animals were used to estimate the genetic parameters of sole lesion susceptibility; 44% of animals recorded a sole lesion at least once during the study period. The heritability of sole lesion susceptibility, on the liability scale, was 0.25 (95% highest density interval = 0.16–0.34). A total of 498 animals were used to estimate the genetic parameters of sole lesion recovery; 71% of animals had recovered between the early and late lactation assessments. The heritability of sole lesion recovery, on the liability scale, was 0.27 (95% highest density interval = 0.02–0.52). The approximate genetic correlation between each trait was −0.11 (95% confidence interval = −0.20 to −0.02). Our results indicate that recovery from sole lesions is heritable. If this finding is corroborated in further studies, it may be possible to use selective breeding to reduce the frequency of chronically lame cows. As sole lesion recovery appears to be weakly genetically related to sole lesion susceptibility, successful genetic improvement of sole lesion recovery would benefit from selection on this trait directly. [ABSTRACT FROM AUTHOR]

Native chicken is a potential genetic source to produce new superior and productive strains. The genetic parameter information is needed as basis data for the selection program to improve the genetic quality of native chickens. This study aimed to estimate the heritability and genetic correlation of quantitative traits in local Indonesian chickens. The material used is the offspring from outbreeding, crossbreeding, and reciprocal crosses of four native chicken lines: White, Lurik, Wareng, and Ranupane. The traits observed were Body Weight (BW), Body Height (BH), Body Length (BL), Body Circumference (BC), Wing Length (WL), Beak Length (BeL), Head Circumference (HC), Thigh-Shank Length (TSL), and Thigh Circumference (TC). The ANOVA and ANCOVA were used to estimate heritability and genetic correlation based on the mating lines of each cage/pen. The mating line significantly affected all the measured traits (P < 0.05). The highest heritability was obtained for BW (h2 = 0.25, moderate category). A positive genetic correlation was found between BW and all other traits except for BeL and HC. In conclusion, body weight can be considered for early selection because it has the highest heritability and positive genetic correlation with other traits related to body size.

ABSTRACT: High mortality and involuntary culling rates cause great economic losses to the worldwide dairy cattle industry. However, there is low emphasis on wellness traits in replacement animals (dairy calves and replacement heifers) during their development stages in modern dairy cattle breeding programs. Therefore, the main objectives of this study were to estimate genetic parameters of wellness traits in replacement cattle (replacement wellness traits) and obtain their genetic correlations with 12 cow health and longevity traits in the Chinese Holstein population. Seven replacement wellness traits were analyzed, including birth weight, survival from 3 to 60 d (Sur1), survival from 61 to 365 d (Sur2), survival from 366 d to the first calving (Sur3), calf diarrhea, calf pneumonia, and calf serum total protein (STP). Single and bivariate animal models were employed to estimate (co)variance components using the data from 189,980 Holstein cattle. The genetic correlations between replacement wellness traits and cow longevity, health traits were calculated by employing bivariate models, including 6 longevity traits and 6 health traits (clinical mastitis, metritis, ketosis, displaced abomasum, milk fever, and hoof health or hoof disease). The estimated heritabilities (± SE) were 0.335 (± 0.008), 0.088 (± 0.005), 0.166 (± 0.006), 0.102 (±0 .006), 0.048 (± 0.003), 0.063 (± 0.004), and 0.170 (± 0.019) for birth weight, Sur1, Sur2, Sur3, pneumonia, diarrhea, and STP, respectively. The majority of the genetic correlations among the 7 replacement wellness traits were negligible. The genetic correlations among Sur1, Sur2, and Sur3 ranged from 0.112 (Sur1 and Sur3) to 0.445 (Sur1 and Sur2) when fitting a linear model (estimates in the observed scale), and from 0.560 (Sur1 and Sur3) to 0.773 (Sur1 and Sur2) when fitting a threshold model (estimates in the liability scale). The genetic correlations between replacement wellness and cow longevity were low (absolute value lower than 0.30), but some of them were significantly different from zero. Compared with other replacement wellness traits, Sur3 and STP had relatively high genetic correlations with cow longevity. Replacement wellness traits are heritable and can be improved through direct genetic and genomic selection. The results from the current study will contribute for better balancing dairy cattle breeding goals to genetically improve dairy cattle wellness in the period from birth to first calving.

ABSTRACT: To devise better selection strategies in dairy cattle breeding programs, a deeper knowledge of the role of the major genes encoding for milk protein fractions is required. The aim of the present study was to assess the effect of the CSN2, CSN3, and BLG genotypes on individual protein fractions (αS1-CN, αS2-CN, β-CN, κ-CN, β-LG, α-LA) expressed qualitatively as percentages of total nitrogen content (% N), quantitatively as contents in milk (g/L), and as daily production levels (g/d). Individual milk samples were collected from 1,264 Brown Swiss cows reared in 85 commercial herds in Trento Province (northeast Italy). A total of 989 cows were successfully genotyped using the Illumina Bovine SNP50 v.2 BeadChip (Illumina Inc.), and a genomic relationship matrix was constructed using the 37,519 SNP markers obtained. Milk protein fractions were quantified and the β-CN, κ-CN, and β-LG genetic variants were identified by reversed-phase HPLC (RP-HPLC). All protein fractions were analyzed through a Bayesian multitrait animal model implemented via Gibbs sampling. The effects of days in milk, parity order, and the CSN2, CSN3, and BLG genotypes were assigned flat priors in this model, whereas the effects of herd and animal additive genetic were assigned Gaussian prior distributions, and inverse Wishart distributions were assumed for the respective co-variance matrices. Marginal posterior distributions of the parameters of interest were compared before and after the inclusion of the effects of the 3 major genes in the model. The results showed that a high portion of the genetic variance was controlled by the major genes. This was particularly apparent in the qualitative protein profile, which was found to have a higher heritability than the protein fraction contents in milk and their daily yields. When the genes were included individually in the model, CSN2 was the major gene controlling all the casein fractions except for κ-CN, which was controlled directly by the CSN3 gene. The BLG gene had the most influence on the 2 whey proteins. The genetic correlations showed the major genes had only a small effect on the relationships between the protein fractions, but through comparison of the correlation coefficients of the proteins expressed in different ways they revealed potential mechanisms of regulation and competitive synthesis in the mammary gland. The estimates for the effects of the CSN2 and CSN3 genes on protein profiles showed overexpression of protein synthesis in the presence of the B allele in the genotype. Conversely, the β-LG B variant was associated with a lower concentration of β-LG compared with the β-LG A variant, independently of how the protein fractions were expressed, and it was followed by downregulation (or upregulation in the case of the β-LG B) of all other protein fractions. These results should be borne in mind when seeking to design more efficient selection programs aimed at improving milk quality for the efficiency of dairy industry and the effect of dairy products on human health.

The objective of this study was to use a random‐effects model of meta‐analysis to merge various heritability estimates of different gas emission traits (methane yield [METY], methane production [METP], carbon dioxide production [CO2], the sum of carbon dioxide and methane production [METP + CO2], METPMETP+CO2 ratio, and oxygen consumption [O2]) and their genetic association with growth and partial efficiency traits in sheep. A total of 53 genetic correlations and 47 heritability estimates from 13 scientific articles were used in the meta‐analysis. The included papers were published between 2010 and 2022. To measure heterogeneity, Chi‐square (Q) test was performed, and the I2 statistic was determined. The average heritability estimates for the studied traits were low to moderate and ranged from 0.137 (for METY) to 0.250 (for METP + CO2). The heterogeneity test of heritability estimates indicated that heritability estimates for METY, O2 consumption, and METPMETP+CO2 had low Q values and non‐significant heterogeneity (p > 0.10). However, the average heritability estimates for other traits experienced significant heterogeneities (p < 0.10). The genetic correlation estimate between METP with O2 was −0.597 (p < 0.05), but its genetic correlations with other gas traits ranged from 0.593 (with METP + CO2) to 0.653 (CO2; p < 0.05). Also, mean estimates of genetic correlation between METP with live weight (LW), feed intake (FI), and residual feed intake (RFI) were 0.719, 0.598, and 0.408, respectively. The genetic correlations of CO2 with performance traits varied from 0.641 (with RFI) to 0.833 (with FI; p < 0.05). This meta‐analysis showed gas emission traits in sheep are under low‐to‐moderate genetic control. The average genetic parameter estimates obtained in this study could be considered in the genetic selection programmes for sheep, especially when there is no access to accurate phenotypic records or genetic parameter estimates for gas emission traits. [ABSTRACT FROM AUTHOR]

The Pacific oyster Crassostrea gigas is the most commonly farmed mollusks worldwide, while its production has been hindered by massive mortalities linked to high temperatures. Selective breeding focusing on thermal tolerance is an attractive option for reducing the impact of massive mortalities, but the genetic basis underlying this trait is currently unknown. Hybridization between C. gigas and C. angulata was conducted and followed by one generation of family selection for thermal tolerance. The genetic parameters for thermal resistance, summer survival and growth and selection response were investigated by using 50 full-sibling families. The mean survival of all families ranged from 30.2 to 69.5%, reflecting the large variation of thermal tolerance in the hybrid population. The estimates of heritability for thermal tolerance were low to moderate, ranging from 0.19 ± 0.03 to 0.27 ± 0.05, confirmed that there is genetic basis for thermal tolerance. The phenotypic (r = 0.537, P < 0.01) and genetic correlations (r = 0.546, P < 0.01) between thermal tolerance and summer survival were positive and significant, while the phenotypic and genetic correlations between growth and survival (including thermal tolerance and summer survival) were positive but low (P > 0.05). High response to selection (ΔG = 36.33%) was observed after one generation of selection for thermal tolerance, and there was also corresponding response (ΔG = 14.46%) for summer survival. These results demonstrated that genetic selection to improve summer survival of oyster may be facilitated by selecting highly heat-tolerant lines. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Kirkuk University for Agricultural Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)