The Girolando breed, a cross between Holstein (Bos taurus) and Gir (Bos indicus), is the primary choice for dairy production in Brazilian tropical regions, as it combines high productive potential with adaptation to the tropical environment. Since the pre-weaning period is crucial for development, this study aimed to evaluate genetic and phenotypic associations among growth, health, and reproductive traits in Girolando cattle. The dataset included 15,311 heifers born between 2017 and 2024. Traits evaluated included birth weight (BW), weaning weight (W70), and average daily gain (ADG); health traits: diarrhoea (CD), pneumonia (BP), umbilical infection (UI), and tick fever (TF); and heifer reproduction: age at first calving (AFC). (Co)variance components were estimated using an animal mixed model under Bayesian inference. Single-trait analyses estimated heritabilities, while three-trait analyses estimated correlations among traits. Heritabilities ranged from low to moderate, with higher values for BW (0.27) and W70 (0.27), and lower for ADG (0.17), AFC (0.07), and diseases (0.04 to 0.13). Favourable genetic correlations were observed between growth and reproductive precocity, specifically ADG × AFC (-0.75), indicating that animals with higher growth performance tend to have reduced AFC. However, UI showed unfavourable genetic correlations with growth traits (BW = 0.75; W70 = 0.69; ADG = 0.50). Given that umbilical infections typically occur in the first weeks of life prior to the main growth period, these associations should be interpreted with caution, as they represent underlying genetic relationships rather than direct linear causality. Phenotypic correlations followed similar patterns but with a lower magnitude. These results confirm that growth, calf health, and reproduction are genetically correlated, reinforcing the importance of integrated selection programs to promote productive efficiency and calf health in tropical dairy systems.
The selection of feed efficient sows effectively converting feed into milk production and body weight gain will contribute to the overall profitability of pig farming systems. Consequently, the aim of the present study was to estimate genetic parameters for feed efficiency indicator traits during the sow lactation period, and to infer respective genetic and phenotypic relationships. Daily feed intake (FI), milk yield (MY), sow weight at farrowing (SWF), litter birth weight (LBW), sow weight at weaning (SWW) and litter weaning weight (LWW) were recorded between 2015 and 2024 on a total of 2418 lactating sows consisting of rotational crosses between German Landrace and German Edelschwein, kept in the University Giessen research herd. The dataset was used to predict different feed efficiency indicator traits including average daily feed intake (ADFI), residual feed intake (RFI), residual gain (RG), residual feed intake and weight gain (RIG), lactation efficiency (LE), feed conversion ratio (FCR) and metabolizable energy for maintenance (MEm). Growth indicator traits were average daily sow weight gain (ADG) and growth rate per litter (GR_L). (Co)variance components for feed efficiency indicator traits, growth indicators and MY were estimated by applying multiple-trait repeatability animal models via Gibbs sampling. Posterior means for heritability estimates (± posterior SD) for feed efficiency indicators were mostly moderate, that is, 0.14 ± 0.02 for ADFI, 0.28 ± 0.04 for RG, 0.19 ± 0.03 for RIG, 0.14 ± 0.02 for RFI, 0.21 ± 0.04 for FCR, 0.11 ± 0.04 for LE and 0.27 ± 0.03 for MEm, accompanied with moderate additive-genetic variation. Milk production and growth indicators displayed moderate posterior heritability estimates with 0.22 ± 0.03 for MY, 0.27 ± 0.02 for ADG and 0.13 ± 0.02 for GR_L. The posterior means of genetic correlations among feed efficiency indicator traits were consistent with their corresponding phenotypic correlations, displaying similar magnitudes and same signs. Largest genetic correlations were estimated between RG and RIG (0.85 ± 0.02) and between FCR and LE (0.96 ± 0.01). The strong genetic correlation between LE and ADG (0.70 ± 0.13) reflects the increased energy demand for milk production during lactation, also expressed in the genetically negative correlation between LE and GR_L (-0.57 ± 0.12). Correlations between MY and ADG were genetically (-0.71 ± 0.08) and phenotypically (-0.40 ± 0.11) negative. Genetic and phenotypic correlations between RFI and ADG close to zero suggest the integration of RFI into breeding indices without compromising growth traits.
Heat stress imposes significant challenges to welfare, health, and productivity of lactating sows due to their limited thermoregulatory capacity and increased metabolic heat load. Genetic selection for improved heat tolerance is a promising yet complex approach, given the large number of biological processes involved in heat stress response. Therefore, the main objective of this study was to unravel biotypes related to thermotolerance in lactating sows using a multivariate approach. A total of 37 traits, including physiological, behavioural, and anatomical measures, as well as climatic resilience indicators derived from automatically recorded vaginal temperature data, were included in the study. Phenotypic data were collected between June and July 2021 in 1645 multiparous Landrace × Large White sows housed under commercial conditions in North Carolina (United States). The additive genetic correlation matrix among these traits was used for performing multivariate analyses (principal component analysis and factor analysis). Heritability estimates ranged from 0.04 for ear skin temperature (TES) to 0.39 for ear area (EA), whereas repeatability estimates ranged from 0.10 for TES to 0.58 for vaginal temperature measured at 12:00 h (TV12h). Genetic correlations were high and positive among surface temperature traits (0.76-0.79) and among vaginal temperature measures and their derived climatic resilience indicators (0.62-0.99), whereas correlations of these temperature-related traits with morphological and behavioural traits were generally low to moderate. Four principal components and common factors were extracted, explaining 71.20% of the total variance of the data. However, a factor analysis using an orthogonal Varimax rotation was superior in isolating distinct and conceptually clear biotypes. The first factor, which explained 38.80% of the total variance, was named "Core body temperature" and was defined primarily by multiple vaginal temperature measures and climatic resilience indicators. The second factor (12.61% of the total variance) was labelled "Heat dissipation measures", which grouped skin temperatures, panting scores, and respiration rate traits. The third factor (11.68% of the total variance) was interpreted as "Vaginal temperature dynamics" and showed both positive and negative loadings, contrasting vaginal temperature variability with its autocorrelation and skewness. Finally, the fourth factor (8.11% of the total variance), referred to as "Stress reactivity and morphology", was defined by a contrast between traits with positive loadings (responsiveness scores, vocalization, and hair density) and traits with negative loadings (body size and body condition). By successfully reducing 37 traits into four interpretable latent profiles, these factors could serve as breeding objectives, steering towards more holistic selection strategies to better understand how pigs adapt to increasingly challenging climatic conditions.
Commercial laying hen breeding programs aim to maximize the performance of crossbred animals. However, selection is usually based on data from pure lines raised in controlled nucleus environments, which may not fully predict the yields under commercial conditions. This study assessed the impact of incorporating crossbred information into genetic evaluations of pure lines. Using stochastic simulations of a two-way crossbreeding scheme, performance in pure lines and crossbred animals was modelled as different traits with an assumed genetic correlation. Nine scenarios were simulated by varying heritability and the genetic correlation between populations. Six levels of crossbred information were tested, ranging from no crossbred data to full use of individual phenotypes and genotypes, as well as cost-effective options based on pooled phenotypes and pooled genotypes. Results showed that strategies using both individual phenotypic and genotypic information achieved the highest response, particularly when the genetic correlation between populations was low and heritability was high. At intermediate correlations, pooling approaches offered a useful balance between performance and cost, while at high correlations, pure line selection performed almost as well as the most informative strategies. These findings highlight the importance of estimating the genetic correlation between pure and crossbred populations and considering cost-effective ways to integrate crossbred information, such as pooled data, to optimize genetic gain in laying hen breeding programs.
ABSTRACT In sows, maternal behaviour is a commercially important trait which affects sow and piglet welfare and the quality and safety of human‐animal interactions, especially if free farrowing of sows without crates is in place. The aim of this study was to evaluate maternal behaviour and to estimate its quantitative genetic parameters in Swiss Landrace sows (SLR). The lactating sows, with an average lactation period of 29–30 days and different parities (one to eleven) at four nucleus farms of the Swiss pig breeding company Suisag underwent behaviour tests and observations during two 12‐week periods. The level of aggressiveness of the sows towards humans was assessed with a standardised Piglet Handling Test and with a Farmers' Aggressiveness Score, using five‐ and three‐point scoring systems, respectively. Additionally, the reaction to the playback of unknown squealing piglets was evaluated using a five‐point scoring system, and vocalising before lying down and time taken to lay down were recorded. Depending on the trait, data were collected on 451–705 sows, most of which had phenotypes available for more than one trait. Number of samples per measure varied between one and six. Given the non‐Gaussian distribution of the traits, variance components were estimated based on Bayesian generalised linear mixed models, including as random effects the sow genetic effects and the sow permanent environmental effects. The additive genetic relationships were either computed from the pedigree (1418 individuals across six generations), the genomic data (from 376 genotyped sows using SNP‐Chip 60 K), or their combination using the H matrix. The credibility intervals of the estimates indicate that all traits were repeatable independently of how the additive genetic relationships were modelled, with estimated repeatabilities varying between 0.18 ± 0.09 (mean ± SD; 95% CI = [0.01, 0.35]) and 0.66 ± 0.04 ([0.58, 0.74]). Both the level of aggressiveness towards humans (pedigree based: Piglet Handling Test: h 2 = 0.44 ± 0.12, [0.22, 0.69]; Farmers' Aggressiveness Score: h 2 = 0.24 ± 0.10, [0.04, 0.45]), and vocalising before lying down were found to be heritable (H matrix based: h 2 = 0.36 ± 0.15, [0.07, 0.63]). We found an unfavourable genetic correlation between the Farmers' Aggressiveness Score and piglet survival traits. Our results suggest that human‐directed aggression is heritable, indicating that improvement of stockpersons' safety and sow welfare through genetic selection is feasible, although piglet survival should be included in a balanced breeding index to ensure progress in both traits.
Inbreeding load is the fraction of the mutation load that is due to recessive action of mutations. This load is only expressed in individual's inbred offspring. As such, inbreeding load contributes to genetic heterogeneity among individuals and can thus be used as a criterion of selection. Future parents could be selected based on their estimated breeding values and their estimated inbreeding load. Here, we evaluated the effectiveness of selection strategies involving inbreeding load in a dairy sheep breeding scheme. We evaluated seven strategies with a stochastic simulation across 15 generations of genetic evaluation and selection. The strategies differed in the criteria of selection (only estimated breeding values, only estimated inbreeding load, both or random) and mate strategies minimising inbreeding load, adjusting estimated breeding values by the expected future inbreeding or random. The strategies were compared in terms of genetic gain, pedigree-based inbreeding coefficients, rate of inbreeding, effective population size, inbreeding depression estimates, and accuracy of selection. Results showed that among the seven strategies, there was no significant difference between the five in which selection was based on estimated breeding values over the 15 years of the simulated dairy sheep breeding scheme. As expected, selection only on the estimated inbreeding load and the random selection and random mating strategy had significantly lower genetic gain than the five strategies in which selection was based on estimated breeding values. To conclude, selecting animals using estimated inbreeding load is feasible. However, the magnitude of inbreeding load effects and its accuracy did not show a clear practical interest. Furthermore, the benefit of using inbreeding load in mate allocation strategies was negligible.
Stayability (STAY) presents limitations in capturing genetic variability among animals with varying calving records up to 76 months of age. Furthermore, because STAY is measured late in life (typically at 6 years of age), it inherently prolongs generation intervals. To address this limitation, we evaluated an alternative approach using random regression models, which incorporate multiple phenotypes recorded throughout an animal's lifespan to generate predictive curves for breeding values. Using calving records from 179,001 Nellore cows, we compared two phenotypic expressions of longevity and aimed to identify the optimal random regression model for predicting breeding values. Ten specific ages were defined for functional longevity expressed as stayability (FLSTAY). At each age, a binary phenotype (1 or 0) was assigned based on whether the cow remained in the herd. Alternatively, for functional longevity associated with the number of calvings (FLNC), the cumulative number of calvings recorded up to each specific age was calculated. Models fitting Legendre orthogonal polynomials ranging from linear to cubic degrees were compared. The cubic polynomial proved most suitable for modelling FLSTAY, whereas the quadratic polynomial provided the best fit for FLNC. Predictive ability metrics of estimated breeding values for FLNC were superior to those for FLSTAY. Heritability estimates for FLNC (0.025-0.074) were consistently higher than those for FLSTAY (0.004-0.045). Similarly, genetic correlations across ages were generally stronger for FLNC, reaching 0.88 between 39 and 75 months (the traditional age for selection). These findings suggest the feasibility of applying random regression models to FLNC, allowing its use as an early selection strategy to improve functional longevity.
Functional longevity (FL) is a complex trait that integrates many attributes directly contributing to the successful retention and productivity of a cow within the herd. Consequently, improving FL in beef cattle increases herd profitability. This study aimed to estimate the genetic associations between two alternative FL definitions and traits related to sexual precocity and growth in Nellore cattle from data comprising 2,607,400 calving records. Two FL traits, evaluated from 27 to 135 months of age, were analysed using random regression animal models: the ability to remain in the herd, evaluated as a binary trait (FLSTAY), and the cumulative number of calvings up to the predefined standard ages (FLNC). Approximate genetic correlations among FL measures, age at first calving (AFC), weaning weight (WW), and yearling weight (YW) were estimated using Calo's method based on the estimated breeding values (EBVs) of individuals with an accuracy of at least 0.60 for both traits. Heritability estimates for FLNC ranged from 0.020 (95% highest posterior density [HPD]: 0.018 to 0.022) to 0.075 (95% HPD: 0.067 to 0.084) and were consistently higher than those for FLSTAY, which ranged from 0.004 (95% HPD: 0.004 to 0.005) to 0.045 (95% HPD: 0.039 to 0.051) across all evaluated ages. The approximate genetic correlations between FLSTAY and FLNC were positive and high (≈0.90) throughout the productive lifespan. Approximate genetic correlations between AFC and both FL measures were favourable, exhibiting a high magnitude at 27 months of age (≈-0.75) and gradually decreasing to values around -0.25. Approximate genetic correlations between WW (direct effect) and FL traits were favourable, starting high at 27 months (≈0.58) and gradually decreasing to 0.06 for FLNC and 0.15 for FLSTAY at older ages. Approximate genetic correlation estimates between WW (maternal effect) and both FLSTAY and FLNC were also favourable (≈0.20) across ages. YW followed a similar trend, showing favourable genetic correlations of ≈0.28 at 27 months that decreased to ≈0.10 near 75 months. Both FL traits exhibit sufficient genetic variability for long-term selection. However, FLNC is recommended over FLSTAY as a selection criterion due to its consistently higher heritability. The favourable genetic associations demonstrate no antagonism between functional longevity and standard growth or sexual precocity traits, supporting the implementation of FLNC as a feasible alternative to traditional single-measure stayability in Nellore breeding programs.
In most sheep production systems, the ideal scenario is for every ewe to lamb and successfully raise twins. However, selection for increased number of lambs born (NLB) is often accompanied by a higher incidence of triplet and higher-order births, which increases management challenges and producer reluctance to intensify selection for this trait. Therefore, there is a need to identify animals who not only exhibit a high twinning rate, but also canalized reproductive performance, characterized by consistent expression of twin births and reduced variability toward extreme litter sizes. In this context, the double hierarchical generalized linear model (DHGLM) provides a mechanism to evaluate both the mean and the dispersion components of a trait, enabling the evaluation of the heteroskedastic residual variance for each animal, and thereby select animals with lower residual variances. Therefore, the main objectives of this study were to estimate (co)variance components and genetic parameters for the mean and dispersion traits related to number of lambs born and twinning rate and to develop a strategy to identify rams whose daughters consistently produce twins. Data from the US Sheep Experiment Station (USSES; Dubois, ID, USA) Polypay flock recorded from 1999 to 2025 included 8061 litters (3115 dams; 293 sires) and the Rambouillet flock included 5948 litters (2333 dams; 228 sires) recorded from 2005 to 2025. The proportion of twin litters have significantly decreased in the Polypay flock while remaining unchanged (p = 0.13) in the Rambouillet flock. For both breeds, the proportion of single lamb litters have decreased while triplets have increased. For sires with a minimum of 15 daughter litters, the average litter size was 1.53 ± 0.55 for 1-year-old ewes and 2.29 ± 0.75 for mature ewes for the Polypay and 1.24 ± 0.43 for 1-year-old ewes and 1.93 ± 0.64 for mature ewes for the Rambouillet. A DHGLM was used to estimate both the mean and dispersion of the NLB3 considering 3 classes (1, 2 or 3+) and NLB for all classes (1 to 5 for Polypay and 1 to 4 for Rambouillet) traits using the DHGLMF90 package. A bivariate animal model was fitted each for NLB3 and NLB, and their associated dispersions (vNLB3 and vNLB) with contemporary group as a concatenation of ewe age category and birth year fitted as a categorical fixed effect and animal (ewe), permanent environment, and the residual as random effects for a model including data from all ewes (ALL) and excluding phenotypic records from 1-year-old ewes (MATURE). The same mean traits and twinning (TWIN; 1 for non-twins and 2 for twins) were evaluated using a threshold model with the THRGIBBSF90 package. Heritability estimates ranged from 0.024 ± 0.012 for vNLB3 analysed using the DHGLM model to 0.110 ± 0.020 for NLB3 analysed using the threshold model for the ALL Polypay dataset and from 0.016 ± 0.014 for vNLB3 analysed using the DHGLM model to 0.094 ± 0.022 for NLB3 analysed using the threshold model for the MATURE Polypay dataset. For the Rambouillet, heritability estimates ranged from 0.020 ± 0.013 for TWIN analysed using the threshold model to 0.150 ± 0.022 for NLB analysed using the threshold model for the ALL dataset and from 0.021 ± 0.012 for vNLB3 analysed using the DHGLM model to 0.151 ± 0.023 for NLB analysed using the threshold model for the MATURE dataset. The estimated breeding values (EBV) were filtered if lower than the square root of heritability, or the equivalent of a single phenotypic record and correlations of EBV among animals were compared. Correlations among all NLB approaches were high (0.80 to 1.00) for both breeds, indicating high agreement between NLB considering differing numbers of classes and also for different models (DHGLM and threshold). Theoretical accuracies were evaluated for all animals and sires with more than 15 daughter litters. The TWIN trait analysed using a threshold model and the vNLB trait analysed using the DHGLM model were selected to represent twinning rate using the MATURE dataset for both breeds. Of the sires, 9.9% of Polypay and 32.1% of Rambouillet had both positive TWIN EBV and negative vNLB EBV, suggesting that progress can be achieved for both increased twins and canalization of number of lambs born. Extension of twinning rate to national genetic evaluation is warranted to meet the needs of US sheep producers.
The Finnish Ayrshire cattle belong to the Nordic Red breeds. The basis of selection in Nordic Red breeds shifted from traditional pedigree-based breeding values to genomic breeding values between 2011 and 2014. Joint genetic evaluation and admixture among the Nordic Red breeds have led to the formation of a composite Nordic Red population; consequently, contemporary Finnish Ayrshire represents an admixed population. We identified recent selection signatures in the Finnish Ayrshire genome using two complementary approaches: the Hudson estimator of Wright's fixation index (FST) and generation proxy selection mapping (GPSM). Hudson FST quantifies population-differentiation between groups, whereas GPSM detects selection signatures within a single population by regressing birth year on SNP genotypes. The aim of this study was to identify temporal allele frequency changes in SNPs consistent with selection during the genomic era and to evaluate their associations with milk production and fertility traits in Finnish Ayrshire. Genotypes were available for 64,160 cows across 46,914 SNPs, and phenotypic data on milk production and fertility traits were available for 49,417 genotyped individuals. Based on Hudson FST, 56 SNPs showed genetic differentiation between cows selected using pedigree-based and genomic information. In addition, 54 SNPs exhibited temporal allele frequency changes consistent with selection according to GPSM. Overall, 11 SNPs were identified by both methods. Of the 54 SNPs, thirteen were associated with the interval from first to last insemination in Finnish Ayrshire heifers. These results suggest that a substantial proportion of SNPs exhibiting temporal allele frequency changes during genomic selection are associated with heifer fertility.
High litter sizes in pigs are associated with lower birth weights and increased within-litter variation, which poses challenges to pig farming. Improving piglet uniformity and survival via genetic selection of terminal boars could be a strategy to enhance these traits. This study evaluated how well estimated breeding values for uniformity and survival of terminal Piétrain sires predict within-litter uniformity and mortality rates in their crossbred offspring from birth until slaughter. We selected six Piétrain boars with contrasting estimated breeding values for uniformity and survival (3 high vs. 3 low) and mated these with 93 hybrid sows, which produced 1421 liveborn piglets. Moderate correlations were observed between crossbred piglets' within-litter uniformity and the paternal estimated breeding values (r = 0.17 to 0.28), indicating that the used estimated breeding values for uniformity and survival of terminal sires have a low predictive value for within-litter uniformity. Nonetheless, significant associations were found between these estimated breeding values and specific survival- and uniformity-related traits, such as pre-weaning survival and uniformity of growth, supporting their potential for selection. When analysing piglets' weights over time, low correlations were observed between the coefficient of variation at birth and later in life (r = 0.05 to 0.24). On the other hand, moderate to high correlations were found between coefficients of variation at weaning and later in life (r = 0.34 to 0.88), indicating stability of the within-litter body weight uniformity after weaning. These findings suggest that pre- and post-weaning within-litter body weight uniformity should be considered as two distinct traits.
The present study aimed to estimate the contributions of direct additive and maternal genetic effects on growth rate and Kleiber's ratio traits in Deccani sheep. Data from 2590 lambs born between 2011 and 2020 at the ICAR-Network Project on Sheep (Deccani) Improvement, Maharashtra, India, were utilised. Growth traits were assessed as average daily gain from birth to weaning (ADG1), weaning to 6 months (ADG2), and 6 to 12 months of age (ADG3), along with the corresponding Kleiber's ratios (KR1, KR2, and KR3). Bayesian inference was employed to estimate (co)variance components and genetic parameters by fitting animal models that included direct additive and maternal genetic effects, as well as significant fixed effects, using BLUPF90 programs. Estimates of direct heritability for ADG1, ADG2, ADG3, KR1, KR2, and KR3 were 0.07 ± 0.04, 0.05 ± 0.03, 0.08 ± 0.07, 0.03 ± 0.01, 0.04 ± 0.01, and 0.12 ± 0.06, respectively. Maternal genetic and permanent environmental effects accounted for up to 9% and 4% of the total phenotypic variance in the studied traits. A negative genetic correlation between direct additive and maternal genetic effects indicated an antagonistic relationship, whereas positive genetic correlations were observed among the growth and Kleiber's ratio traits. The study results suggested that although additive genetic variation for these traits is low, incorporating maternal effects in selection strategies could enhance growth performance in Deccani sheep populations.
Heat stress is an increasingly important challenge for the performance and welfare of equine athletes, particularly in competitions conducted across diverse climatic conditions. Understanding the genetic basis of performance responses to increasing thermal load is therefore essential to support robust genetic evaluation and sustainable selection strategies. The objective of this study was to evaluate barrel racing performance of Brazilian Quarter Horses across thermal environments defined by two widely used indicators, the temperature-humidity index (THI) and the wet bulb globe temperature (WBGT), and to estimate genetic parameters associated with baseline performance and environmental sensitivity. A total of 351,993 barrel racing time (BRT) records from 13,960 Quarter Horses were analyzed. Segmented regression models were applied to least squares means to identify heat stress thresholds, while random regression models with polynomial functions were used to estimate covariance components, genetic parameters, and correlations along the thermal gradients. Distinct thermal thresholds were identified for both indices, at THI = 74.7 and WBGT = 23.6, beyond which performance deteriorated more rapidly. Heat stress conditions were observed in 31.15% and 20.71% of barrel racing events according to the THI and WBGT thresholds, respectively, emphasizing the practical relevance of the evaluated thermal gradients. Random regression models assuming continuous thermal variation provided the best overall fit to the data. Additive genetic variance remained relatively stable across thermal environments, whereas permanent environmental and residual variances increased under higher thermal load. Mean heritability under thermoneutral conditions was approximately 0.21 for THI and WBGT, declining modestly to 0.17-0.18 under extreme heat stress. Genetic correlations between thermoneutral and extreme environments remained high for both indices (at approximately 0.97), indicating strong genetic continuity of performance and no substantial reranking of estimated breeding values across the thermal gradient. Overall, THI and WBGT yielded highly consistent results in terms of thresholds, genetic parameters, and selection outcomes. Although WBGT showed slightly greater sensitivity under extreme conditions, THI offered smoother response patterns and clear operational advantages due to its simplicity and ease of calculation. No evidence of substantial genotype-by-environment interaction was detected for BRT. However, the results suggest that incorporating genetic tolerance to heat stress as a complementary selection criterion may help sustain barrel racing performance under increasingly challenging climatic conditions.
During the last decade, there has been a growing interest for local and endangered breeds as they are often seen as more resilient and healthier than mainstream breeds. They can also provide high added-value products like meat or dairy products. To better preserve these breeds, it is of main importance to characterize their genetic diversity and have an insight of historical gene flow with more mainstream breeds. In this study, we focused on the genetic relationships of five red-pied cattle breeds: the east Belgian red and white (EBRW); the red-pied of the Ösling (RPO), from Luxembourg; the deep red (DR) and the Meuse-Rhine-Yssel (MRY), both from the Netherlands; and the German red and white dual purpose (RDN). The EBRW, RPO and DR breeds have an official European endangered status. We first investigated the pedigree completeness of available genotyped animals as well as their complex historical relationships through the analysis of common ancestors. We also compared pedigree and SNP-based inbreeding coefficients, defined as the sum of homozygosity-by-descent segments (HBD). We then dived into genomic relationships through a classical multi-dimensional scaling (MDS) of genotyped animals and their admixture. The pedigree analyses showed the complex gene flow between all breeds and that the RPO breed was the most connected to other breeds. Results also showed that the level of inbreeding was so far not an issue in all five breeds even if some animals, for example, in EBRW and MRY breeds, showed higher inbreeding levels than the average of their breeds. Finally, the MDS and admixture analysis also highlighted complex gene flow between the studied breeds and that they may be considered as a genetic continuum. This genomic proximity has the potential to improve genetic evaluations of local breeds by the inclusion of information from more mainstream breeds like MRY and RDN.
Phenotypic responses to selection in growth performance of Ross 308 broiler breeders were evaluated using longitudinal body weight records from hatch to 64 weeks across four successive generations (G1-G4). The effects of generation, sex, and their interaction on body weight were examined, and sex-specific growth trajectories were modelled using eight nonlinear growth functions (Gompertz, Richards, Logistic, Brody, von Bertalanffy, Weibull, Hossfeld, and López). Generation and sex exerted highly significant effects on body weight across most ages (p ≤ 0.0001), with males consistently heavier than females and sexual dimorphism becoming evident after 4 weeks of age. At 60 weeks, fourth-generation birds exhibited higher body weights than first-generation birds (males: 5036.5 vs. 4744.3 g; females: 4152.2 vs. 3858.3 g), indicating progressive phenotypic improvement across generations. A significant generation × sex interaction (p ≤ 0.05) indicated that the magnitude of sexual dimorphism varied among generations. Model performance was evaluated using the coefficient of determination (R2), adjusted R2 ( R adj 2 ), mean square error (MSE), root mean square error (RMSE), Akaike's Information Criterion (AIC), and Bayesian Information Criterion (BIC). All nonlinear models adequately described the growth patterns of Ross 308 broiler breeders. Among them, the Gompertz function generally provided the best fit across generations and sexes (R2 = 0.9978-0.999; R adj 2 = 0.9974-0.999; MSE = 1521.5-1988.5; RMSE = 39.03-44.59), while the Richards model effectively captured sex- and generation-specific variation in growth trajectories. Inflection point weights ranged from 1819.5 to 2012.5 g in males and from 1433.7 to 1573.4 g in females. Predicted values were closely aligned with observed data across models, indicating good descriptive performance of the fitted functions. These findings highlight consistent phenotypic improvement in growth traits across generations and demonstrate that the Gompertz model provides a practical and robust framework for describing and monitoring growth dynamics in broiler breeder populations, supporting its use in selection and management decisions.
The pressing requirement for agricultural systems to adopt climate change adaptation strategies specifically designed for tropical environments underscores the significance of implementing sustainable bull breeding practices in beef cattle operations. We consider alternative approaches to generation of single- and multi-breed genomic predictions for a population of Brahman (N = 1051), Santa Gertrudis (N = 929) and UltraBlack (N = 844) bulls with genotypes at high-density and phenotypes for scrotal circumference (SCC; mean ± SD = 31.82 ± 4.32 cm), sheath score (SHS; 3.70 ± 1.98) and percent normal sperm (PNS; 63.34% ± 28.22%). We examined five genomic prediction models: three single breed and two multi-breed. The later contained a multi-breed genomic relationship matrix computed without (GRM_u) or adjusting (GRM_a) for breed-specific allele frequencies. Bias, dispersion and accuracy of the genomic predictions across the five models was computed based on cross-validation and using the LR method. The elements of the multi-breed GRM_u revealed anomalies including a multi-modal distribution of diagonal and off-diagonal elements with all diagonal values above one (range: 1.022 to 1.524) and averaging 1.163. Instead, GRM_a values were consistent with expectations: diagonals with a single mass around one (range: 0.844 to 1.391) and off-diagonal values with a single mass around zero. Estimates of heritability (h2 ± SE) with the GRM_u model were 0.501 ± 0.037, 0.458 ± 0.037 and 0.362 ± 0.030, for SCC, SHS and PNS, respectively; while h2 estimates using GRM_a were comparable for SCC (0.434 ± 0.037), and SHS (0.439 ± 0.038), and possibly lower for PNS (0.226 ± 0.032). Estimates of genetic correlation (rg) were similar for both models, except for the rg between SHS and PNS which moved from -0.095 ± 0.119 using GRM_u to -0.298 ± 0.113 using GRM_a. For all traits, the correlation between GEBV from GRM_u and GRM_a was > 0.90, indicating similar ranking of animals regardless of the relationship matrix used. However, a random cross-validation scheme showed that using GRM_a increased GEBV accuracies from 0.550 to 0.571 (or 3.7%) for SCC, from 0.496 to 0.509 (or 2.6%) for SHS and from 0.335 to 0.423 (or 26.2%) for PNS. Multi-breed genomic predictions for tropical bull fertility are feasible alternative to individual single-breed evaluation systems. Furthermore, the computational effort required to adjust the genomic relationship matrix for breed-specific allele frequencies is justified by the significant improvement in prediction accuracy, ultimately enhancing the selection of bulls in tropical environments. Collectively these results enable very early in-life selection for bull fertility traits, supporting genetic improvement strategies currently taking place within tropical beef production systems in northern Australia.
Sustainable beef production requires identifying animals with superior feed efficiency to reduce environmental impact and production costs. This study aimed to estimate heritability and genetic correlations between residual gain (RG) and growth, reproductive, carcass and feed efficiency traits in Nellore cattle. A total of 217,333 phenotypic records from animals born between 1980 and 2024, raised in diverse Brazilian regions, were analysed using Bayesian inference with a multi-trait mixed animal model. Heritability estimates for reproductive traits were low, while feed efficiency, growth and carcass traits showed moderate heritabilities. Residual gain exhibited a moderate positive genetic correlation with adjusted weight at 450 days (W450), supporting its potential as a selection criterion for growth efficiency. Genetic correlations between RG and carcass, reproductive and feed efficiency traits were generally low or near zero. However, a negative low genetic correlation and a moderate positive phenotypic correlation with early conception probability indicate complex effects on reproduction. Selection for RG may increase yearling weight and maintenance energy requirements, which can reduce early reproductive performance under restricted nutritional conditions, presenting trade-offs to consider in extensive systems. Conversely, RG may be particularly suitable for intensive systems such as feedlot or finishing operations, where nutritional management can mitigate these limitations. The negative genetic correlation between RG and residual feed intake further highlights RG's ability to identify animals that grow efficiently without increased feed intake. These results confirm that RG is a largely independent trait and shows sufficient genetic variability to respond to selection in Nellore cattle. Using it as a selection criterion can enhance feed efficiency without negatively impacting other economically important traits.
Recent theoretical work shows that the potential of genetic selection to reduce the prevalence of infectious diseases is much larger than expected from classical quantitative genetic theory, due to indirect genetic effects that arise in the transmission process. However, to fully benefit from these indirect effects, we need to estimate genetic parameters and breeding values, which requires statistical methods tailored to the transmission process. Here, we evaluate Generalized-Linear-Mixed Models (GLMMs) implemented using software commonly used in animal breeding to estimate genetic parameters and breeding values for susceptibility of hosts to infection, using simulated data of epidemics. Longitudinal records of individuals' infection state provide information on the order of infection, as well as on the exposure dose of non-infected animals. Such information can be harnessed to estimate genetic parameters for susceptibility, and can be included in a GLMM as a so-called offset. Therefore, we used longitudinal records of individual infection state to assess the impact of sampling interval, population structure, infection characteristics, and model formulation on the estimated genetic variance and breeding values for susceptibility. The results show that a GLMM fitted to longitudinal records of individual binary infection state can produce accurate and unbiased estimates of genetic variance, as well as good prediction accuracies of breeding values for susceptibility to an infectious disease. Of the data requirements, the time interval between consecutive observations on individual infection state was the main factor affecting estimation, while group size had a limited effect. The required observation interval depends on the infection and recovery rates of individuals. The GLMM thus seems an accurate and easily implementable model to estimate genetic parameters and breeding values for susceptibility when dense longitudinal records on individual infection status are available.
Sheep production contributes to a secure and diverse food and fibre supply in the United States, with growing ethnic diversity strengthening demand. Katahdin is a composite hair-type sheep breed developed in the United States that has become the most popular breed in many regions of the country and the first one to have genomic selection implemented in its breeding program. Therefore, the main objectives of this study were to estimate variance components of reproductive traits, including number of lambs born (NLB), number of lambs weaned (NLW), age at first lambing (AFL), and interval from first to second lambing (LI), in Katahdin sheep using the AIREML method and the single-step Genomic Best Linear Unbiased Prediction (ssGBLUP) approach, and to identify genomic regions and candidate genes associated with these traits. The datasets used consisted of 127,536 animals in the pedigree, phenotypic records of 56,128 parities from 24,067 ewes, and genomic data from 10,032 animals with 30,308 single-nucleotide polymorphisms (SNP) after quality control. Analyses were performed using the BLUPF90 family of programs. We observed low heritability estimates for all studied traits (0.09 ± 0.00 for NLB, 0.08 ± 0.00 for NLW, 0.09 ± 0.01 for AFL, and 0.08 ± 0.01 for LI). The genetic correlations between the traits ranged from 0.17 ± 0.02 (AFL and LI) to 0.79 ± 0.02 (NLB and NLW). All traits were found to be highly polygenic with all 14 significant SNP on eight (OAR) chromosomes (3, 6, 7, 8, 9, 12, 13, and 15) having small effects on the total variability on the traits. These SNP were located near or within 18 candidate genes: four genes associated with NLB (AAK1, GFPT1, SLC23A2, and GDAP1), four with NLW (ARHGAP18, TTLL2, UNC93A, and GPR31), six with AFL (NAP1L5, FAM13A, HS3ST1, CCDC181, NME7, and BLZF1), and four with LI (TAF4, CDH4, CADM1, and SEL1L). These candidate genes have been previously associated with fertility, embryonic development, growth, disease resistance, and climatic adaptation traits. Our findings indicate that fertility and reproduction traits in Katahdin sheep can be improved through direct genetic selection. Genetic improvement for these traits will benefit from genomic selection as more accurate estimates of breeding values for selection candidates can be obtained at a younger age. Although the studied traits are influenced by a complex interplay of genetic and environmental factors, the candidate genes identified enabled a better understanding of the biological mechanisms underlying reproductive performance in Katahdin sheep.
Recent studies suggest that variations in milk yield during lactation can serve as a metric for assessing individual resilience, with minimum fluctuations reflecting greater resilience in cows. With automatic milking systems, multiple records can be obtained from many cows on a daily basis. Given that animals are continually exposed to a range of stressors throughout their productive life, identifying resilience traits is crucial for preserving both their productive and reproductive capacities. We studied two resilience indicators: natural log-transformed variance (ln(σ2)) and lag-1 autocorrelation (rlag) of the daily deviations in milk yield recorded from the automatic milking system, with average daily milk yield incorporated as an additional trait to assess the correlations between the resilience indicators and milk yield. The resilience indicators were derived from three fitted lactation curves: the Wilmink curve, the penalised quadratic spline and the penalised cubic spline. The data consisted of 18,549,535 daily milk records of 37,118 Fleckvieh, 7322 Holstein-Friesian and 2784 Brown Swiss cows in 1070 Austrian dairy herds. The data were collected for lactation periods of up to 350 days across multiple parities. Additionally, the resilience indicators were calculated based on days 11-50 of lactation, as the transition period and early lactation are crucial moments for the cows. Genetic analyses were conducted using univariate and bivariate analyses to estimate heritability (h2) and genetic correlations between the traits. Estimated breeding values of the indicator traits obtained from the genetic analysis were correlated with routinely estimated breeding values and official indexes of the cows, including dairy, health, functional and conformation traits. The resilience indicators were scaled to negatives (-ln(σ2) and -rlag) to compute these correlations, so that positive values indicate better resilience. The h2 ranged between 0.09 and 0.20 for ln(σ2) and between 0.04 and 0.06 for rlag, with the highest estimates observed when the penalised cubic spline was used. Generally, the h2 decreased when only the early lactation period was considered. Genetic correlations of the resilience indicators with existing traits were low to moderate, with stronger correlations observed with -ln(σ2) than in -rlag, particularly when estimated with the dairy traits. The negative correlations with milk yield, ranging from -0.13 to -0.45 for -ln(σ2) and -0.03 to -0.23 for -rlag suggest that selecting for the resilience traits may result in a reduction in milk yield. This study demonstrated that deviations derived from fluctuations in lactation curves are heritable. Among the two resilience indicators evaluated, -ln(σ2) emerged as the more suitable trait for selection, considering its h2 and positive correlation to health and functional traits. These findings underscore the potential for incorporating resilience traits into breeding programs while emphasising the need to balance improving resilience with milk production, considering the probable risk of reduced milk production in cows.
