Permanent Environmental Effects Research Articles

Bayesian approach to estimate variance components and genetic parameters of average daily gains and Kleiber ratios in crossbred cattle.

The present study aimed to estimate variance components and genetic parameters of the average daily gains from birth to 3-months (ADG1), 3- to 6-months (ADG2), 6- to 9-months (ADG3) and 9- to 12-months (ADG4) of age and corresponding Kleiber ratios (KR1, KR2, KR3 and KR4, respectively) in Jersey crossbred calves. Data for this study were collected from National Dairy Research Institute, Eastern Regional Station, Kalyani, West Bengal, India during 2013 to 2021. Genetic parameters for the traits were estimated using Bayesian procedure through Gibbs sampling by fitting six animal models. The deviance information criterion (DIC) was employed to determine the most appropriate model for each trait under investigation. Direct heritability estimates for ADGs and KRs ranged from 0.19 (ADG3) to 0.59 (ADG2) and 0.14 (KR3) to 0.72 (KR1), respectively. Significant maternal heritability ( ) was observed for average daily gains (17-24%) and Kleiber ratios (13-18%) at different age intervals under the best model. Estimates of permanent environmental effects ( ) of dam for ADG1, ADG2, KR2 and KR4 were only 1-4% of the total phenotypic variance in this study. Large negative estimates of correlations, ranging from -0.87 to -0.98, between direct and maternal ) effects for ADGs and KRs (except KR3) at different age intervals were observed. Total heritability ( ) and maternal repeatability ) of the studied traits ranged 0.05-0.28 and 0.01-0.14, respectively. The moderate to high heritability estimates for all traits in this study indicate the possibility of genetic progress for these traits through selective breeding program.

Genetic Parameter Estimates for Growth of Hāpuku (Groper, Polyprion oxygeneios) in Land-Based Aquaculture Using Random Regression Models

Hāpuku (Polyprion oxygeneios) is a promising candidate for aquaculture production in New Zealand. Methods for spawning, juvenile production, and growout to harvest entirely on land, where water quality, pathogens, environmental impacts, and genetic “pollution” can be tightly controlled, have been developed, and genetic improvement to optimise land-based production is the obvious next step. However, estimates of genetic parameters are required to design a rigorous, disciplined, and effective selective breeding program. By using existing data consisting of irregularly spaced repeated measurements of fork length and live body weight collected on wild-collected founders and two generations of captively reared progeny, we evaluated the species’ genetic potential for improvement in growth. We first tested a range of univariate random regression models to identify the best-fitting models for these data. Subsequently, using a bivariate model, we estimated variance components for growth trajectories of fork length and whole body weight. With one to six records available per fish, the best-fitting univariate models included only a fixed effect for contemporary groups and fixed and random genetic third-order Legendre polynomials. More complex models that included full-sib family and/or permanent environmental effects produced unacceptable constrained and/or non-positive-definite solutions. Both traits are moderately heritable at all stages of the growout phase (~0.4–0.5), and the genetic correlation patterns between daily breeding values estimated via the covariance function are different for length and weight. Genetic correlations for length between all pairs of age-specific breeding values are positive and strong (>0.7) and change gradually and smoothly with increasing temporal separation. For weight, these correlations deteriorate more rapidly with increasing time lags between measurements and become negative for some age pairings. We conclude that random regression analyses are a valuable tool for extracting genetic information from irregularly spaced repeated measurements of fish size, speculate that emerging technologies for high-throughput genotyping and phenotyping will add to the value of this approach in the near future, and reason that a breeding strategy that rigorously takes into account the potentially unfavourable genetic correlations between breeding values for weight at some ages will further adapt hāpuku to land-based systems and enhance the profitability commercial-scale production.

313 Many farmers want a prediction of future performance, not necessarily breeding potential

Abstract High heritability is a characteristic of many performance traits in cattle translating to a relatively strong correlation between an (accurate) estimated breeding value for an animal and its (future) production potential. However, the broad sense heritability (i.e., numerator in the formula includes both additive and non-additive genetic variance) is often larger than the narrow sense heritability (i.e., numerator includes only the additive genetic variance) especially for low heritability traits like reproductive performance. Moreover, the repeatability of a trait can be greater again, the difference between the heritability and repeatability is the inclusion of a permanent environmental variance in the numerator. Accurate and pertinent omic-based animal-level features can contribute to an increase in narrow sense heritability owing to a possible reduction in the residual variance; such data can also provide insights into both non-additive genetic variance and the permanent environmental variance. Even at the level of the contemporary group, omic-level data from the likes of bulk blood/saliva/milk sample, can improve how systematic environmental effects are modeled in the mixed model equations thus also reducing the residual variance. The greater heritability will contribute to a strengthening of the correlation between future phenotypic performance potential and estimated additive genetic merit. Importantly, omic measures for individual animals can help predict the permanent environmental effect of that animal capturing the yet untapped contributor to phenotypic variance. Irrespective, predictions need to be replaced by prescriptions; end users are inundated with data yet crave actionable information. Animal breeders have a lush (pardon the pun!) history of distilling estimates of genetic merit for a plethora of traits into a single value per animal (i.e., breeding indexes); this is the first step in transitioning from predictions to prescriptions. Given the familiarity of end users with breeding indexes, the potential exists to develop production indexes to rank commercial animals on future expected performance taking cognizance of genetic and non-genetic effects. Such production indexes align well with the associated breeding index but where the estimates of genetic merit for the traits in question are replaced by estimates of production merit; these estimated production values are the sum of the additive genetic, non-additive genetic and permanent environmental effects. The weighting factors in the production indexes are more short-term using current costs and prices as opposed to breeding indexes which are longer term. In comparison with the breeding indexes, the production indexes can include ancillary information like gender, age, and month of calving/birth. Like breeding indexes, they are updated in real time. Examples of the application of genome-enabled production indexes are guides for valuing animals for purchase or culling.

87 Estimation of genetic parameters for activity levels in North American Holstein cattle

Abstract High-producing dairy cows face various challenges during their productive life, including metabolic and infectious diseases, heat stress, and intensive management practices. Therefore, dairy cattle breeding programs have begun to focus on novel traits related to the overall resilience of the cows. Resilience has been defined as the ability of an animal to be minimally affected or to recover quickly from environmental disturbances while maintaining a certain level of productivity. In dairy cattle, resilience can be evaluated using measurements of longitudinal indicator traits, such as activity level, which are repeatedly measured throughout the lifetime of the animal. Variability in activity (defined in this study as average steps per hour and daily steps taken) might be a key indicator of resilience, due to activity of contemporary group animals deviating from normal behavior in less resilient cattle. Before resilience indicators are defined based on variability in certain longitudinal traits, there is a need for evaluating the genetic background of the source variable. Therefore, the primary objective of this study was to estimate variance components and genetic parameters of activity traits in lactating Holstein cows. The data was collected from a large dairy farm located in Indiana, using milking systems. Records from 6,332 Holstein cows for the traits of average activity per hour and number of daily steps were used in the analyses. The models used for the estimation of variance components included the significant (P < 0.05) fixed effects of contemporary group defined based on the concatenation of calving year and season, parity number (1, 2, 3+), pregnancy group (0 mo, 1-3 mo, 4-6 mo, and 7-9 mo), and days in milk (2-wkclasses), and the random effects of direct additive genetic, permanent environment, and residual effects. Genetic parameters were estimated for average activity per hour and daily steps for all parities together. Heritability estimates were calculated based on single-trait repeatability models and the (AI) REML method implemented in the BLUPF90+ software. Daily steps taken had a heritability (± SE) of 0.05 (± 0.02) and a repeatability of 0.29 (± 0.02). Activity per hour had a heritability estimate of 0.12 (± 0.04) and a repeatability of 0.57 (± 0.01). These results indicate that activity level is heritable in North American Holstein cattle and can be added to selection indexes if proven to be a key auxiliary trait for improving dairy cattle sustainability. As a next step, we will investigate the genetic relationship of activity traits with other relevant traits and derive resilience indicators based on variability in activity level.

Genetic evaluation of udder health traits in the Spanish Holstein Cows

The aims of this study were to estimate the genetic parameters of clinical mastitis (CM) and somatic cell score (SCS) traits, and to compare the performance of genetic evaluations of CM traits using univariate and bivariate analyses (CM-SCS). Data was edited according to the Udder Health Golden Standard harmonization and then, 6 CM traits and 6 SCS traits were considered, as the result of combining 3 lactation classification (1, 2, ≥ 3) and 2 milking periods (early, late). The linear mixed animal models included the ratio of period at risk as a covariate, herd-year of calving, month of calving, and lactation-age as fixed effects, and the permanent environmental effect for traits of ≥ 3 lactations. Prevalence of CM in early lactation was similar regardless the lactation number (5-6%) and the estimated heritabilities were 0.01. Prevalences in late lactation ranged from 10% to 24% and heritabilities ranged from 0.03 to 0.05. Estimated heritabilities of SCS ranged from 0.06 to 0.16 with univariate analyses. Somatic cell count (therefore its log-transformation SCS) showed higher probability of identify correctly healthy cows than infected cows but there was still up to 36% of healthy cows for CM not detected by SCS. Genetic correlations between CM-SCS traits ranged from 0.36 to 0.95, and SCS in lactation 3 and later did not add extra information to SCS in second lactation for predicting CM. Regarding reliabilities of estimated breeding value (EBV) for CM traits, bivariate CM-SCS analyses led to substantial increases with respect to the single-trait model for sires (7-12% more in first lactation and 16-28% more for second lactation). Sire's rank correlations for CM between univariate and bivariate analyses (0.47-0.92) suggest that discarding sires could be more accurate than selecting candidates for sires of dams. We can conclude that SCS in first lactation could be useful to supplement CM data in first and second lactations to improve udder health genetic evaluation.

Estimation of Genetic Parameters of Early Growth Traits in Dumeng Sheep.

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.

Analysis of the genetic variance of fibre diameter measured along the wool staple for use as a potential indicator of resilience in sheep

BackgroundThe effects of environmental disturbances on livestock are often observed indirectly through the variability patterns of repeated performance records over time. Sheep are frequently exposed to diverse extensive environments but currently lack appropriate measures of resilience (or sensitivity) towards environmental disturbance. In this study, random regression models were used to analyse repeated records of the fibre diameter of wool taken along the wool staple (bundle of wool fibres) to investigate how the genetic and environmental variance of fibre diameter changes with different growing environments.ResultsA model containing a fifth, fourth and second-order Legendre polynomial applied to the fixed, additive and permanent environmental effects, respectively, was optimal for modelling fibre diameter along the wool staple. The additive genetic and permanent environmental variance both showed variability across the staple length trajectory. The ranking of sire estimated breeding values (EBV) for fibre diameter was shown to change along the staple and the genetic correlations decreased as the distance between measurements along the staple increased. This result suggests that some genotypes were potentially more resilient towards the changes in the growing environment compared to others. In addition, the eigenfunctions of the random regression model implied the ability to change the fibre diameter trajectory to reduce its variability along the wool staple.ConclusionsThese results show that genetic variation in fibre diameter measured along the wool staple exists and this could be used to provide greater insight into the ability to select for resilience in extensively raised sheep populations.

Estimates of Genetic Parameters, Growth Curve, and Environmental Effects for Nellore Cattle in the Pantanal.

The objective was to estimate the growth curves and genetic parameters using random regression methodology for Nellore cattle raised in Pantanal, MS, Brazil (6974 calves; n = 53,233 weights), with at least four weighings per individual. The model considered direct and maternal genetic additives and maternal permanent environmental effects at random. Orthogonal Legendre polynomials of cubic order were used to fit the growth curve. Analyses of variance were performed using the GLM procedure. The model used contained the fixed effects of sex, year of birth, farm, and the covariates calf birth month (linear and quadratic) and cow age at calving (linear and quadratic). The adjusted mean weight at 120 days of age was 93.43 ± 19.78 kg, and for 205 days of age, it was 180.42 ± 26.58 kg. Animals born in the dry season had a higher average weight [kg] (219.57 vs. 211.78, 3.7% higher) and, consequently, had higher weights at 646 days of age. Estimates of direct heritabilities (h2a) ranged from 0.35 to 0.75 (high magnitudes), and maternal heritabilities (h2m) along the trajectory of low magnitudes ranged from 0.03 to 0.08, respectively. The use of random regression to evaluate beef animals allows for adjusting the growth curve and selecting the best animals to be the parents of future generations.

Genetic parameter estimates for growth traits in Texel sheep of Brazil

The present study aimed to estimate the genetic parameters for direct and maternal influences on birth weight (BW), weaning weight (WW) and postweaning weight (PW) of Texel sheep, raised in an extensive system from Brazil. Data file used in this study consisted of 834 body weight records of 834 Texel lambs (376 males and 458 females) born from 2020 to 2021, offspring of 475 ewes and 45 rams. Multivariate animal model was conducted to estimate the variance components, heritability and genetic correlations using the restricted maximum likelihood method in ASReml-R software, while the effects of fixed factors were analyzed by the general linear model procedure of statistical analysis system. Five different animal models, with or without maternal genetic effect, maternal permanent environment effect and covariance between additive genetic and maternal genetic effects, were fitted on the growth traits data of the Texel sheep population. AIC and BIC test results were used to evaluate the best fit models for growth traits. The effect of lamb's sex was significant on BW, WW and PW while birth type and year of lambing effects were on all traits (P < 0.05). Model 4, incorporating direct additive genetic, maternal additive genetic and maternal permanent environmental effects showed the minimum AIC estimate for all growth traits. The direct additive genetic heritability for BW, WW and PW were 0.11 ± 0.03, 0.37 ± 0.05 and 0.31 ± 0.08, respectively and maternal heritability for these traits were 0.00 ± 0.00, 0.04 ± 0.02 and 0.00 ± 0.00, respectively. The total heritability estimates for BW, WW and PW were 0.11 ± 0.03, 0.39 ± 0.05 and 0.31 ± 0.08, respectively. The estimates of the direct additive genetic correlation between BW-WW, BW-PW and WW-PW were 0.68 ± 0.12, 0.66 ± 0.12 and 0.78 ± 0.07, respectively. The maternal permanent environmental correlation between BW-WW, BW-PW and WW-PW were 0.38 ± 0.27, 0.29 ± 0.20 and 0.74 ± 0.16, respectively. Phenotypic correlation ranged from 0.43 ± 0.03 to 0.75 ± 0.02. It is concluded that due to the existence of genetic variation for BB, WW and PW and strong genetic correlation among them, genetic progress for these traits is possible by selection in Texel sheep from Brazil.

Examining across year genotype by environment interactions for production and reproduction traits in Merino sheep

Variation in feed resource availability within production systems can cause genotype by environment interactions that change the ranking of the best animals to select between environments. Mediterranean environments have high variation in pasture growth between years that could cause genetic by environment interactions for sheep production traits. Therefore, we estimated heritabilities for live weight, fleece weight, fibre diameter and number of lambs weaned in six years from 2000 to 2005 and correlations between years comparing multivariate analysis and random regression analysis. We compared 3 methods: 1 multivariate analysis estimating (co)variances for traits in each year, 2 Random regression estimated (co)variances for intercept and slope for traits as repeated measurements fitted against average pasture growth in each year and 3. Random regression fitted against corrected average performance of animals in each year. Random regression was estimated with an order of polynomial of one for additive genetic variance and zero for permanent environmental effects. This combination of polynomials was the best fit based on Bayesian information criterion. We estimated heritabilities for each year and correlations between years using records from 3299 pedigreed Merino ewes managed at Katanning in Western Australia. There were 4651 records for adult live weight, 6750 for adult clean fleece weight, 6965 for adult fibre diameter, and 7774 for number of lambs weaned across all 6 years. Number of lambs weaned had more genotype by environment interactions than other traits, with fibre diameter and fleece weight having genotype by environment interactions between only a few years. Based on Bayesian information criterion values, multivariate analysis fit the data better for live weight, fleece weight and fibre diameter. Additionally, random regression estimated higher genetic correlations between years than multivariate analysis suggesting there was not enough flexibility in the random regression analysis, which used only first order polynomials, to fit differences between years. Pasture growth across years did not explain differences in performance for traits across years. Therefore, for number of lambs weaned, random regression using corrected average performance was a better fit than average pasture growth. For other traits, more years or a better indicator of variation in performance within and between years are required to use random regression for genotype by environment interactions.

Estimation of the threshold for heat stress and genetic features for milk yield in Mehsana buffaloes in India

Heat stress is one of the primary environmental factors that harm both the productivity and health of buffaloes. The current study was conducted to estimate the threshold of temperature humidity index (THI)1 and genetic features for milk yield of first-lactation Mehsana buffaloes using an univariate repeatability test-day model. The data included 130,475 first lactation test-day milk yield (FLTDMY) records of 13,887 Mehsana buffaloes and the daily temperature and humidity. The statistical model included herd test day as fixed effects, days-in-milk (DIM) classes, age of the animal, as well as random factors such as the additive genetic effect (AGE) of animal in general conditions (intercept), AGE of the buffaloes subjected to heat stress (slope), permanent environmental effect of animal in general conditions (intercept), permanent environmental effect of animal under heat stress conditions (slope) and random residual effect. It was expected that the general effects and the heat-tolerance effects would be correlated, represented by the present investigation's repeatability models. The variance components of FLTDMY in the present study were computed using the REML method. The threshold for THI was 78. At the THI below the threshold, the heritability estimated for the FLTDMY trait was 0.29, and the additive genetic variance (AGV) for heat stress conditions was 0. At THI of 83, AGV for heat stress conditions was highest for FLTDMY. The genetic correlation of general AGE to heat-tolerant AGE was −0.40. The results indicated that a consistent selection for milk production, avoiding the thermal tolerance, may diminish the thermal tolerance capacity of Mehsana buffaloes.

Genetic relationships between behavioral traits and feed efficiency traits in lactating Holstein cows

The evaluation of dairy cow feed efficiency using residual feed intake accounts for known energy sinks. However, behavioral traits may also contribute to the variation in feed efficiency. Our objective was to estimate the heritability and repeatability of behavioral traits and their genetic correlations with feed efficiency and its components in lactating Holstein cows. The first data set consisted of 36,075 daily rumination and lying time records collected using a SMARTBOW ear tag accelerometer (Zoetis, Parsippany, NJ) and 6,371 weekly feed efficiency records of 728 cows from the University of Wisconsin-Madison. The second data set consisted of 59,155 daily activity records, measured as number of steps, recorded by pedometers (AfiAct; S.A.E. Afikim, Kibbutz Afikim, Israel), and 8,626 weekly feed efficiency records of 635 cows from the University of Florida. Feed efficiency and its components included dry matter intake, change in body weight, metabolic body weight, secreted milk energy, and residual feed intake. The statistical models included the fixed effect of cohort, lactation number, and days in milk, and the random effects of animal and permanent environment. Heritability estimates for behavioral traits using daily records were 0.19 ± 0.06 for rumination and activity, and 0.37 ± 0.07 for lying time. Repeatability estimates for behavioral traits using daily data ranged from 0.56 ± 0.02 for activity to 0.62 ± 0.01 for lying time. Both heritability and repeatability estimates were larger when weekly records instead of daily records were used. Rumination and activity had positive genetic correlations with residual feed intake (0.40 ± 0.19 and 0.31 ± 0.22, respectively) while lying time had a negative genetic correlation with this residual feed intake (-0.27 ± 0.11). These results indicate that more efficient cows tend to spend more time lying and less time active. Additionally, less efficient cows tend to eat more and therefore also tend to ruminate longer. Overall, sensor-based behavioral traits are heritable and genetically correlated with feed efficiency and its components and, therefore, they could be used as indicators to identify feed efficient cows within the herd.

Maternal genetic effects throughout the life of the dam in Pirenaica beef cattle. a random regression model approach

In beef cattle, dams play a crucial role in shaping the pre- and postnatal environment for the growth of their offspring. Acknowledging the substantial impact of maternal influence on the early development of calves, researchers utilize maternal animal models. These models take into account both maternal genetic and permanent environmental effects, operating under the assumption that these influences remain constant throughout the productive life of the cow. Nevertheless, it cannot be ruled out that these genetic and environmental effects may evolve throughout the lifespan of the cows. Therefore, this study aims to describe the changes in genetic and environmental maternal effects over the productive lifespan of cows. To accomplish this goal, we utilized random regression models, incorporating the age of the dam effect, maternal genetic effects, and environmental permanent effects using Legendre orthogonal polynomials. Additionally, the analytical model incorporated a covariate to adjust for the calf’s age at recording, a two-level sex effect, a random herd-year-season effect, and an additive direct genetic effect associated linked to the calf. The dataset comprised information from dams aged between 2 and 16 years, resulting in a final database that comprised weight records of 58 332 calves from 21 673 dams. The average weight at 90 days was 135.0 ± 39.3 kg, and the mean age of the dam at calving was 7.03 ± 3.41 years. We evaluated models incorporating 2, 3, 4, 5, and 6 orthogonal polynomials alongside the standard maternal animal model. Afterward, we selected the model with five orthogonal polynomials based on the Akaike Information Criteria. The Restricted Maximum Likelihood estimates within this model indicated a direct heritability of around 0.50, and a maternal heritability ranging between 0.15 and 0.25, exhibiting a consistent increase between 4, 5 to 13 years. The genetic correlation estimates between direct and maternal genetic effects remained stable at approximately −0.55 across the lifespan of the cows. Furthermore, maternal genetic correlations between different ages of the dam decreased to around 0.7 for more distant age points. The maternal permanent correlations were notably lower, occasionally even reaching negative values, suggesting variability in environmental influence on maternal effects over the productive lifespan of the cow. Finally, the model enables the prediction of breeding values for the maternal genetic effects of the cow across its lifespan, providing opportunities for innovative selection strategies on the maternal side.

Direct and maternal genetic parameters for growth traits in Jersey crossbred cattle

Abstract Growth data on Jersey crossbred calves, maintained at ICAR-National Dairy Research Institute, Eastern Regional Station, Kalyani, Nadia, West Bengal, India, were collected and analysed to assess the influence of maternal effects on growth traits of calves. Traits considered for this study were birth weight (BW) and weights at 3 months (W3M), 6 months (W6M), 9 months (W9M) and 12 months (W12M) of age. Least-squares analyses were employed to obtain the effects of non-genetic factors on the traits of interest. Determination of influence of maternal effects on growth traits was estimated by fitting three univariate animal models (including or excluding maternal effects) using Bayesian approach. The most appropriate model for each trait was selected based on Deviance Information Criterion. Direct heritability (h2) estimates for BW, W3M, W6M, W9M and W12M were 0.31 ± 0.08, 0.26 ± 0.10, 0.48 ± 0.10, 0.44 ± 0.11 and 0.39 ± 0.14, respectively, under the best model. Permanent environmental maternal effects (c2) varied from 0.04 to 0.12 for all traits. Existence of maternal effects for all ages reflects the importance of maternal components for these traits. Moderate to high heritability estimates for growth traits indicate the possibility of modest genetic progress for these traits through selection under prevalent management system.

Genetic parameters for milk yield and reproductive traits in Honduran Holstein cattle.

The aim of this study was to estimate the (co)variance components and genetic parameters for milk yield adjusted to 305d (MY305), calving-to-conception interval (CCI), number of services per conception (NSC) and calving interval (CI) of Honduran Holstein cows, by fitting a bivariate animal model using Maximum Restricted Likelihood procedures. Model included the fixed effects of calving number, the contemporary calving group (farm-season-year of calving and the cow age as covariate). The estimated means and standard deviations for MY, CCI, NSC and CI were, 5098.60 ± 1564.32kg, 168.27 ± 104.71 days, 2.46 ± 1.69 services, and 448.73 ± 109.16 days, respectively; and their estimated heritabilities were 0.21 ± 0.05, 0.03 ± 0.028, 0.02 ± 0.024 and 0.06 ± 0.04, respectively. The genetic correlations between MY305 and CCI, NSC and CI were positive and antagonist, with values of 0.64 ± 0.52, 0.99 ± 0.56, and 0.32 ± 0.24 respectively. Even though moderate to low heritability was estimated for MY305, systematic selection for milk yield, with a reduction in reproductive efficiency, if considered as the only selection criterion is important to be considered. By including reproductive traits and considering permanent environment effects into the breeding program, might yield a slow, but constant and permanent improvement over time.

Estimation of the genetic parameters of sheep growth traits based on machine vision acquisition

In the realm of animal phenotyping, manual measurements are frequently utilised. While machine-generated data show potential for enhancing high-throughput breeding, additional research and validation are imperative before incorporating them into genetic evaluation processes. This research presents a method for managing meat sheep and collecting data, utilising the Sheep Data Recorder system for data input and the Sheep Body Size Collector system for image capture. The study aimed to investigate the genetic parameter changes of growth traits in Ujumqin sheep by comparing machine-generated measurements with manual measurements. The dataset consisted of 552 data points from the offspring of 75 breeding rams and 399 breeding ewes. Six distinct random regression models were assessed to pinpoint the most suitable model for estimating genetic parameters linked to growth traits. These models were distinguished based on the inclusion or exclusion of maternal genetic effects, maternal permanent environmental effects, and covariance between maternal and direct genetic effects. Fixed factors such as individual age, individual sex, and ewe age were taken into account in the analysis. The genetic parameters for the yearling growth traits of Ujumqin sheep were calculated using ASReml software. The Akaike information criterion, the Bayesian information criterion, and fivefold cross-validation were employed to identify the optimal model. Research findings indicate that the most accurate models for manually measured data revealed heritability estimates of 0.12 ± 0.15 for BW, 0.05 ± 0.07 for body slanting length, 0.03 ± 0.07 for withers height, 0.15 ± 0.12 for hip height, 0.11 ± 0.11 for chest depth, 0.13 ± 0.13 for shoulder width, and 0.53 ± 0.15 for chest circumference. The optimal models for machine-predicted data showed heritability estimates of 0.1 ± 0.09 for body slanting length, 0.14 ± 0.12 for withers height, 0.55 ± 0.15 for hip height, 0.34 ± 0.15 for chest depth, 0.26 ± 0.15 for shoulder width, and 0.47 ± 0.16 for chest circumference. In manually measured data, genetic correlations ranged from 0.35 to 0.99, while phenotypic correlations ranged from 0.07 to 0.90. In machine data, genetic correlations ranged from −0.05 to 0.99, while phenotypic correlations ranged from 0.03 to 0.84. The results suggest that machine-based estimations may lead to an overestimation of heritability, but this discrepancy does not impact the selection of breeding models.

Early growth performance in the Murciano-Granadina goats: insights from genetic and phenotypic analyses

Abstract This study investigates the genetic and phenotypic aspects of early growth performance in the Murciano-Granadina goat breed, using data collected between 2016 and 2022 from a private dairy farm in Ghale-Ganj city, located in the southern area of Kerman province, Iran. Pedigree and data information were collected on several early body weight traits, including birth weight (BW), weaning weight (WW), average daily gain (ADG), Kleiber ratio (KR) and growth efficiency from birth to weaning (GE). Nine univariate animal models included direct additive genetic effects and different combinations of maternal effects were compared by using Akaike information criterion (AIC). Among the tested models, the best genetic analysis model for BW, included direct additive, maternal additive, maternal permanent and maternal temporary environmental effects. The best model for ADG, KR and GE included direct additive, maternal permanent and litter effects. For WW, the best model was determined to be one that included direct additive and maternal additive genetic effects. The estimated direct heritabilities were low values of 0.04, 0.07, 0.08, 0.05 and 0.07 for BW, ADG, KR, GE and WW, respectively. The estimates of genetic correlations among the studied traits were positive and low to high in magnitude which ranged from 0.11 for BW-KR to 0.91 for BW-GE. The phenotypic correlations ranged from 0.03 for KR-WW to 0.87 for ADG-KR. The positive correlations observed among the studied growth traits of the Murciano-Granadina goat breed indicate no negative genetic or phenotypic changes associated with selection for these traits.

78 Estimates of genetic parameters for weight, wool, and carcass traits of Rambouillet sheep

Abstract The Rambouillet sheep breed is the foundation to most western range sheep in the U.S. Through the National Sheep Improvement Program (NSIP), Rambouillet breeders seek to improve economically relevant traits. However, the parameters used in the NSIP genetic evaluation were last estimated two decades ago. Our aim was to re-estimate genetic parameters for body weight (BW), carcass, and wool traits. Data were provided by NSIP, which included BW at birth (BWT; n = 28,817), weaning [WWT; n = 5,633; age: 68 (SD 16) day], early post-weaning [PWTE; n = 23,303; 128 (SD 22) d], late post-weaning [PWTL; n = 7,689; 201 (SD 45) d] and as yearlings [YWT; n = 5,830; 375 (SD 33) d]. Yearling ultrasound scans of 12th rib backfat depth (YCF; n = 2,447) and eye muscle depth (YEMD; n = 2,457) were also available. Yearling wool traits were fiber curvature (YFC; n = 6,009), fiber diameter (YFD; n = 9,880), fiber diameter coefficient of variation (YFDCV; n = 8,803), greasy fleece weight (YGFW; n = 11,859) and staple length (YSL; n = 11,270). The pedigree consisted of 36,297 individuals. Animals with phenotypic records were progeny of 333 sires and 1,727 dams. A succession of univariate animal models was fitted. The most comprehensive model included direct and maternal additive genetic effects, their genetic covariance, and maternal permanent environmental and residual effects. Phenotypes were pre-adjusted for birth-rearing type category and, as covariates, dam age and age at recording. Contemporary group—a concatenation of flock, year, season, management group, sex, and, excluding BWT, recording date—was the only fixed effect fitted in the model. Model selection was based on the log-likelihood ratio test, and Akaike and Bayesian information criteria. The data structure, namely dams and their daughters and granddaughters contributing data, allowed reliable testing of the maternal effect terms. The direct-maternal genetic covariance did not explain variation for any trait (P &amp;gt; 0.18). The most parsimonious model for BW traits included direct and maternal additive genetic, and maternal permanent environmental, effects. For carcass and wool traits, only direct additive genetic effects were included in the final model. Such was unsurprising, as maternal effects often diminish as animals age. For the final models fitted (Table 1), direct heritabilities (± SE) for body weights ranged from 0.14 ± 0.01 (BWT) to 0.38 ± 0.04 (PWTL); the maternal heritabilities ranged from 0.04 ± 0.02 (PWTL; YWT) to 0.13 ± 0.01 (BWT). For carcass traits, direct heritabilities were 0.13 ± 0.04 (YCF) and 0.31 ± 0.05 (YEMD). For wool traits, they ranged from 0.37 ± 0.02 (YSL) to 0.58 ± 0.02 (YFD). These re-estimated parameter values are consistent with those used by NSIP, with the exception of PWTL (greater) and YSL (lower). With genomic data accumulating in Rambouillet, further refinements in parameter estimation are planned.

Genetic parameters for mid-infrared-spectroscopy-predicted mastitis phenotypes and related traits.

Genetic improvement of udder health in dairy cows is of high relevance as mastitis is one of the most prevalent diseases. Since it is known that the heritability of mastitis is low and direct data on mastitis cases are often not available in large numbers, auxiliary traits, such as somatic cell count (SCC) are used for the genetic evaluation of udder health. In previous studies, models to predict clinical mastitis based on mid-infrared (MIR) spectral data and a somatic cell count-derived score (SCS) were developed. Those models can provide a probability of mastitis for each cow at every test-day, which is potentially useful as an additional auxiliary trait for the genetic evaluation of udder health. Furthermore, MIR spectral data were used to estimate contents of lactoferrin, a glycoprotein positively associated with immune response. The present study aimed to estimate heritabilities (h2) and genetic correlations (ra) for clinical mastitis diagnosis (CM), SCS, MIR-predicted mastitis probability (MIRprob), MIR + SCS-predicted mastitis probability (MIRSCSprob) and lactoferrin estimates (LF). Data for this study were collected within the routine milk recording and health monitoring system of Austria from 2014 to 2021 and included records of approximately 54,000 Fleckvieh cows. Analyses were performed in two datasets, including test-day records from 5 to 150 or 5 to 305 days in milk. Prediction models were applied to obtain MIR- and SCS-based phenotypes (MIRprob, MIRSCSprob, LF). To estimate heritabilities and genetic correlations bivariate linear animal models were applied for all traits. A lactation model was used for CM, defined as a binary trait, and a test-day model for all other continuous traits. In addition to the random animal genetic effect, the fixed effects year-season of calving and parity-age at calving and the random permanent environmental effect were considered in all models. For CM the random herd-year effect, for continuous traits the random herd-test day effect and the covariate days in milk (linear and quadratic) were additionally fitted. The obtained genetic parameters were similar in both datasets. The heritability found for CM was expectedly low (h2 = 0.02). For SCS and MIRSCSprob, heritability estimates ranged from 0.23 to 0.25, and for MIRprob and LF from 0.15 to 0.17. CM was highly correlated with SCS and MIRSCSprob (ra = 0.85 to 0.88). Genetic correlations of CM were moderate with MIRprob (ra = 0.26 and 0.37) during 150 and 305 days in milk, respectively and low with LF (h2 = 0.10 and 0.11). However, basic selection index calculations indicate that the added value of the new MIR-predicted phenotypes is limited for genetic evaluation of udder health.

Genetic parameter estimates for methane emission from breath during lactation and potential inaccuracies in reliabilities assuming a repeatability versus random regression model

Methane emissions will be added to many national ruminant breeding programs in the coming years. Little is known about the covariance structure of CH4 traits over a lactation, which is important for optimizing recording strategies and establishing optimal genetic evaluation models. Our aim was to study CH4 over a lactation using random regression (RR) models, and to compare the accuracy to a fixed regression repeatability model under different phenotyping strategies. Data were available from repeated measurements of CH4 concentrations (ppm) recorded in the feed bins of milking robots on 52 commercial dairy farms in the Netherlands. In total, 36,370 averaged weekly records were available from 4,664 cows. Genetic parameters were estimated using a fixed regression model, and a RR model with first- to fifth-order Legendre polynomials for the additive genetic and within-lactation permanent environmental effect. The mean heritability (± SE) was 0.17 ± 0.04, and the mean within-lactation repeatability was 0.56 ± 0.03. The genetic correlations between DIM were high and ranged from 0.34 ± 0.36 to 1.00 ± <0.01. Permanent environmental correlations showed large deviations and ranged from -0.73 ± 0.08 to 1.00 ± <0.01. With a large number of full lactation daughter CH4 records per bull, the reliability was not sensitive to using the fixed versus the RR model. However, when shorter periods were recorded at the start and end of the lactation, the fixed regression model resulted in a loss of reliability up to 28% for bulls. Assuming the fixed model when the true (co)variance structure is reflected by the RR model, more than twice as long of a recording from the start of lactation was required to achieve maximum reliability for a bull. Thus, a too simplistic model could result in implementing too little recording, and in lower genetic gains than predicted from the reliability.