Maternal Permanent Environmental Effects Research Articles

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.

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.

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.

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.

Sex-specific genetic parameter estimates of body weight in Mazandaran indigenous chickens.

Body weight is an economically important trait in poultry that shows sexual dimorphism (SD). In the present study, variation in SD in Mazandaran native chickens was investigated in terms of the (Co) variance components and genetic parameters of body weight between males and females. Studied traits were body weights at hatch (BW1), 8 weeks (BW8) and 12 weeks of age (BW12). Also, for weight at sexual maturity (WSM) covariance components were only estimated in females. Cross-sex direct and maternal correlations were also estimated for studied traits except for WSM. For this purpose, a deep 21-generation pedigree and body weight data (57,576 BW1, 72,925 BW8, 62,727 BW12 and, 42,496 WSM) were used. Evaluation of SD of body weight was performed using six bivariate animal models with and without considering the genetic and permanent maternal environmental effects under the restricted maximum likelihood method in WOMBAT software. Model with direct additive genetic effects and maternal genetic effects without covariance between them was identified as the best model for BW1 and BW8. The Model including direct additive genetic effects and permanent maternal environmental effects was the best model for BW12 and WSM. Direct heritability (h2 ) estimates for BW1, BW8 and, BW12 were, respectively, 0.05 ± 0.013, 0.17 ± 0.02 and, 0.25 ± 0.03 in males and, 0.05 ± 0.012, 0.15 ± 0.01 and 0.21 ± 0.01 in females. Also, the direct heritability of WSM based on univariate analysis in females was estimated to be 0.40 ± 0.01. Maternal heritability ( ) varied from 0.39 ± 0.01 (BW1) to 0.04 ± 0.009 (BW8) in males, and 0.36 ± 0.10 (BW1) to 0.04 ± 0.006 (BW8) in females. The correlation between direct genetic effects between males and females for BW1 was not significantly different from one. The direct genetic correlation between the two sexes for BW8 and BW12 was significantly different from 1 concluding that these traits are dimorphic in terms of direct genetic effects and therefore independent selection in both sexes is possible.

Delineating maternal influence in regulation of variance in major economic traits of White Leghorns: Bayesian insights.

Proper variance partitioning and estimation of genetic parameters at appropriate time interval is crucial for understanding the dynamics of trait variance and genetic correlations and for deciding the future breeding strategy of the population. This study was conducted on the same premise to estimate genetic parameters of major economic traits in a White Leghorn strain IWH using Bayesian approach and to identify the role of maternal effects in the regulation of trait variance. Three different models incorporating the direct additive effect (Model 1), direct additive and maternal genetic effect (Model 2) and direct additive, maternal genetic and maternal permanent environmental effects (Model 3) were tried to estimate the genetic parameters for body weight traits (birth weight, body weight at 16, 20, 40 and 52 weeks), Age at sexual maturity (ASM), egg production traits (egg production up to 24, 28, 40, 52, 64 and 72 weeks) and egg weight traits (egg weight at 28, 40 and 52 weeks). Model 2 and Model 3 with maternal effects were found to be the best having the highest accuracy for almost all the traits. The direct additive genetic heritability was moderate for ASM, moderate to high for body weight traits and egg weight traits and low to moderate for egg production traits. Though the maternal heritability (h2mat) and permanent environmental effect (c2mpe) was low (<0.1) for most of the traits, they formed an important component of trait variance. Traits like egg weight at 28 weeks (0.14±0.06) and egg production at 72 weeks (0.13±0.07) reported comparatively higher values for c2mpe and h2mat respectively. Additive genetic correlation was high and positive between body weight traits, between egg weight traits, between consecutive egg production traits and between body weight and egg weight traits. However, a negative genetic correlation existed between egg production and egg weight traits, egg production and body weight traits, ASM and early egg production traits. Overall, a moderate positive genetic correlation was estimated between ASM and body weight traits and ASM and egg weight traits. Based on our findings, we can deduce that maternal effects constitute an important source of variation for all the major economic traits in White Leghorn and should be necessarily considered in genetic evaluation programs.

Estimation of (Co) Variance Components and Genetic Parameters for Pre- and Post-Weaning Growth Traits in Dağlıç Sheep.

The goal of this study was to estimate (co) variance components and genetic parameters for pre- and post-weaning growth traits in Dağlıç sheep, considering the direct additive genetic, maternal genetic, and maternal permanent environmental effects, with different statistical models. The information of 21,735 native Dağlıç lambs born between 2011 and 2021 was used to estimate (co) variance components by the Average Information-Restricted Maximum Likelihood algorithm. The results showed that the most suitable model was Model 3 for birth weight (BW), average daily gain (ADG), and weaning weight (WW). Model 4 was the most appropriate for weight at three (W3), weight at six (W6), and weight at twelve months of age (W12). The direct heritabilities for BW, W3, ADG, WW, W6, and W12 were 0.35 ± 0.02, 0.36 ± 0.03, 0.27 ± 0.02, 0.22 ± 0.02, 0.47 ± 0.05, and 0.47 ± 0.05, respectively. Genetic and phenotypic correlations amongst the traits were in the range of 0.103 ± 0.008 to 0.995 ± 0.002. These results can be used for the improvement of growth traits in the Dağlıç breed of sheep through selection.

133 Genetic Parameter Estimation for Dry Matter Required in Angus Cattle

Abstract Dry matter intake (DMI) is critical to determine feed efficiency in cattle. However, this trait is expensive and difficult to measure on an individual basis. An alternative to overcome such limitations is to approximate DMI by using mathematical models to estimate the dry matter required (DMR) for animals originally fed in groups. High genetic correlations (e.g., rg&amp;gt;0.95) have been informed between DMI and DMR. Thus, DMR has been proposed as an indicator trait for DMI in genetic evaluations. Few reports exist about genetic parameters associated to DMR and, when available, estimates have been obtained with datasets of limited size (~1,500 animals with phenotypes). Additionally, previous reports have focused on older animals during the finishing phase, such that mathematical models employed to derive DMR have not explicitly accounted for the presence of weaner calves on the data. Therefore, the objective was to estimate genetic parameters for DMR using a moderately large dataset that included weaning and yearling Angus bulls. A total of 8,121 records of estimated DMR were available for analyses. From these records, 6,324 belonged to weaned males whose phenotype was obtained using an equation proposed by NRC to predict DMR for calves. The 1,797 remaining records pertained to yearling bulls for which a different NRC equation was employed. First, a single-trait animal model was implemented to estimate genetic parameters for DMR, with contemporary group (defined as year of test and pen within the testing facility), test length and age of the animal when entering to the test (linear covariate) being included as fixed effects, while animal was considered a random effect. Afterwards, a bivariate model that included weaning weight (WW) as a correlated trait was implemented. In this model, the same effects were included for DMR but for WW, fixed effects included sex, weaning contemporary group and age of dam (BIF classes), while direct genetic, maternal genetic, and maternal permanent environmental effects were considered as random. For both models, a reduced pedigree was used to estimate variance components by tracing back 5 generations from animals having phenotypes. A total of 12,212 and 56,023 animals were included in the univariate analysis and the bivariate analysis, respectively. Heritability (h2) estimates (± SE) for DMR were 0.43 (± 0.04) and 0.49 (± 0.03) under univariate and bivariate model, respectively. Genetic correlation between DMR and WW was 0.69 (± 0.03), with a h2 estimate of 0.29 (± 0.01) for WW. Heritability estimates for DMR obtained with both models resembled those of DMR reported in literature. Results suggested that substantial additive genetic variation exists for DMR and, consequently, indirect genetic progress for DMI can be attained if selecting for DMR. Further studies are required to validate DMR as an indicator trait for DMI in larger Angus populations.

128 Reviewing the Definition of Mortality in Broiler Chickens

Abstract Mortality has been a trait of importance in livestock species, especially for poultry, as birds continue to become more efficient and grow larger, and mortality rates have shown an increase at the phenotypic level. Mortality at different stages may be due to different genetic, environmental, and biological factors; however, in most cases, selection can be based on overall mortality. This study aimed to investigate alternative models and trait definitions of mortality by exploring the classification of mortality into different life stages and including a maternal effect in the model. Data provided by Cobb-Vantress, Inc. included mortality records from three separate lines of chicken making up about three generations worth of data. The datasets included records for mortality (0-1) and weekly mortality (0-10) recorded on individuals during their first ten weeks of life. Due to low incidence levels of weeks seven through ten for weekly mortality, those birds were combined with those that survived the first six weeks. Three models with different trait definitions were considered in this study. They were compared against a benchmark model with overall survival as trait definition (M0). Each line was evaluated separately for each model investigated, and the traits under different definitions were treated as binary traits. Thus, threshold models were used to estimate variance components under a Bayesian approach via the Gibbs sampling algorithm implemented in Gibbsf90+ software. The first model and trait definition (M1) investigated was a single trait model with weekly mortality classified into a binary response to whether the bird died within the first six weeks of life (BMORT). The second model and trait definition (M2) investigated was a bivariate model that classified weekly mortality into early (EMORT; weeks 1 to 3) and late (LMORT; weeks 4 to 6) mortality. The heritabilities ranged from 0.11 to 0.19 for M1, 0.10 to 0.21 for EMORT, and 0.15 to 0.17 for LMORT. For both models investigated, heritabilities were greater than that of M0, ranging from 0.03 to 0.09. The second model was additionally run with the maternal effect (M3) composed of the additive maternal and maternal permanent environmental (mpe) effects to investigate any potential effect on the estimated variance components. The maternal heritability and the percentage of phenotypic variance explained by mpe ranged from 0.02 to 0.03 and 0.01 to 0.04, respectively. Including the maternal effect considerably decreased direct heritability for EMORT but not for LMORT, indicating the maternal effect plays an important role in early mortality. The genetic correlations between EMORT and LMORT were low, ranging from 0.23 to 0.44; therefore, it may be necessary to include both traits in the selection index to make better-informed decisions. Selecting against early and late mortality may help improve overall livability in broiler chickens.

Genetic and environmental parameters for early growth and yearling traits of the Elsenburg South African Mutton Merino resource flock

The Elsenburg South African Mutton Merino (SAMM) resource flock has been maintained at the Elsenburg Research Farm in the Western Cape, South Africa since 1955. Early growth and lamb survival data (record numbers from 9512 for weaning weight or WW to 13101 for lamb survival LS) over the 66-year period from 1955 to 2020 and yearling weight and wool data (record numbers from 1351 the coefficient of variation of fibre diameter or CVFD to 3505 for clean yield or CY) over a 38-year period from 1983 to 2020 were used to estimate genetic and environmental parameters for the flock. The ASREML package was used to estimate fixed effects and genetic parameters. Birth year, sex, birth type and age of dam significantly (P < 0.001) influenced the weight traits and LS. The interaction between birth year and sex was evident in most early growth traits. Direct single-trait heritability estimates amounted to 0.11 ± 0.02 for birth weight (BW), 0.04 ± 0.01 for WW, 0.03 ± 0.01 for LS, 0.28 ± 0.04 for yearling weight (YW), 0.62 ± 0.04 for CY, 0.36 ± 0.04 for clean fleece weight (CFW), 0.33 ± 0.05 for staple length (SL), 0.56 ± 0.06 for CVFD, and 0.65 ± 0.04 for fibre diameter (FD). Maternal heritability estimates were 0.17 ± 0.02 for BW, and 0.06 ± 0.01 for WW. Maternal permanent environmental effects amounted to 0.08 ± 0.02 for BW, 0.04 ± 0.01 for WW, 0.04 ± 0.01, 0.07 ± 0.02 for YW and 0.05 ± 0.02 for CFW. Litter effects were 0.22 ± 0.01 for BW, 0.18 ± 0.02 for WW and 0.14 ± 0.01 for LS. Multi-trait heritability estimates compared well with the single-trait estimates. Genetic correlations for the weight traits in the three-trait analyses ranged from −0.23 ± 0.12 for BW and YW to 0.81 ± 0.09 for WW and YW. Genetic, environmental, and phenotypic correlations amongst yearling weight and wool traits in the six-trait analyses were low to moderate and mostly within the ranges from previous literature for wool breeds. It should be feasible to select for a balanced breeding objective based on economic considerations in the flock.

Effects of prenatal heat stress on birth weight and birth weight genetic parameters in German Holstein calves

The aim of this study was to infer the effects of heat stress (HS) during late gestation of dams on phenotypes and on direct and maternal genetic parameters for birth weight (BiW). We considered 171,221 Holstein calves kept in 56 large-scale co-operator herds. For a clear separation of maternal effects, only calves from dams with at least 3 offspring were included in the analyses. The genotype data set comprised 41,143 SNPs from 1,883 Holstein bulls. Temperature-humidity indices (THI) during the last 8 wk of gestation were calculated in each herd to reflect prenatal HS. A further prenatal HS descriptor was the first principal component (PC1) from principal component analysis considering the daily THI during the last 56 d of gestation. Regression coefficients of BiW on prenatal THI during the last 12 wk of gestation and PC1 were estimated in 13 consecutive phenotypic analyses. The strongest BiW decline was −0.63 kg per standardized THI, identified during 50 to 56 d before birth. A reaction norm model with weekly prenatal THI or PC1 nested within maternal genetic and maternal permanent environmental effects was defined to infer maternal sensitivity in response to prenatal THI alterations. Direct BiW heritabilities were close to 0.33 in the course of prenatal THI. Maternal BiW heritabilities marginally increased from 0.07 to 0.08 with increasing THI. Genetic correlations between maternal genetic effects at maximum HS levels and remaining THI were larger than 0.95, indicating the absence of genotype by time-lagged HS interactions. In contrast, maternal permanent environmental correlations between BiW at prenatal THI indicating HS with BiW at remaining THI substantially declined with increasing THI distances. Hence, from a herd management perspective, avoiding HS during the dry period of the dams will contribute to a slight increase in fetus growth.

Heritability and environmental influence on pre-weaning traits in Kilis goats

Birth and weaning weights, average daily weight gain, and Kleiber ratio are important indicator traits in selection decision. The phenotypic expression of these traits is determined by the genetic background, environmental effects, and their interactions. The objective of this study was to estimate genetic parameters regarding birth (BW) and weaning weights (WW) and average daily weight gain (ADWG), Kleiber ratio (KR), and obtain the effects of sex, birth type, herd, and year. The data consisted of 2274 Kilis goats with pedigree information obtained from 53 bucks and 774 does in 4 generations. The restricted maximum likelihood (REML) procedure was conducted with an animal linear mixed model. Sex, birth type, herd, and year were found to be statistically significant (p value < 0.001) for all traits. Moderate direct heritabilities (ha2) for BW, WW, ADW, and KR were found to be as 0.18 ± 0.03, 0.50 ± 0.04, 0.47 ± 0.04, and 0.37 ± 0.05, respectively. The proportion of maternal permanent environmental effect (c2) to the total phenotypic variance (σ2p) was estimated as 0.00 ± 0.00, 0.12 ± 0.02, 0.11 ± 0.02, and 0.18 ± 0.03 for BW, WW, ADWG, and KR, respectively. The genetic, phenotypic, and environmental correlations between the pre-weaning growth traits were found to be ranging from - 0.02 to 0.99. Thus, our study suggests moderate heritabilities and positive and relatively high genetic correlations among the observed pre-weaning growth traits. These results have implications in terms of providing rapid genetic progress for these traits in breeding programs of Kilis goats.

Study of genetic parameters for pre-weaning growth traits in inner Mongolia white Arbas cashmere goats.

Inner Mongolia Arbas white cashmere goats is a dual-purpose breed for producing cashmere and meat. In recent years, its meat has become more and more popular among consumers because of rich nutrients and delicious flavor. Therefore, it is particularly important to study the genetic and non-genetic factors affecting the early growth traits and estimate variance components of pre-weaning growth traits of Inner Mongolia Albas white cashmere goats. A total of 37487 kidding records such as birth weight (BWT), weaning weight (WWT), average daily gain from birth to weaning (ADG) and Kleiber ratio (KR) from 343 sires and 7296 dams were used in this study. The most appropriate model was chosen on the basis of likelihood ratio test by fitting six models which excluding or including maternal genetic, maternal permanent environmental effects. The parameters were estimated under the most appropriate model using AIREML method by WOMBAT software. With the best model (Model 6), heritability estimates were 0.0435, 0.0911, 0.0932 and 0.2339 for BWT, WWT, ADG and KR traits, respectively. Maternal heritability estimates were 0.0143, 0.0246, 0.0220 and 0.0186 for BWT, WWT, ADG, and KR traits respectively. The correlation between different traits was estimated with the most suitable model by using bivariate analysis method. The direct additive genetic correlation among the traits ranged from -0.026 (BWT~KR) to 0.772 (ADG~KR). The maternal permanent environment correlation is between -0.289 (BWT-KR) ~0.900 (WWT-ADG). Results indicated that maternal effects and direct-maternal genetic covariance should be considered in any program aimed at improving pre-weaning growth traits to have an accurate genetic evaluation. In addition, positive and medium to high genetic correlations generally exist among WWT, ADG and KR due to the existence of genetic variation for early growth traits. The results showed that the genetic progress of these traits could be slowly through selection except for KR.

Genetic parameters for mouth size and their influence on growth traits in pasture-raised Nelore cattle.

Evaluating traits that allow breeders to increase production efficiency in beef production systems is important. The mouth size score is a trait easily measured and implemented by breeders. Bite size is related to mouth size in beef cattle and is a determinant of daily feed intake of pasture-raised animals, influencing their growth. The aim of this study was to estimate genetic parameters for mouth size (MS), weaning weight (WW) and postweaning weight gain (PWG) of Nelore cattle and to evaluate the influence of the interaction between MS and WW on PWG. Phenotypic records of 134,282 Nelore animals born between 1995 and 2019 were used. Variance components were estimated using multi-trait animal model with the Bayesian method. The model included the contemporary group as fixed effect, age at measurement of the trait as linear covariate, and direct additive genetic and residual effects as random effects. For WW, random maternal and maternal permanent environmental effects were added to the model. A Bayesian approach was used to analyze the interaction between WW clusters and MS. The heritability estimates were 0.24, 0.15 and 0.23 for MS, WW and PWG, respectively. The genetic correlation between variables studied ranged from 0.24 to 0.46. The results suggest that animals with a larger mouth tend to have greater PWG, demonstrating the positive influence of MS score on the postweaning performance of cattle. The direct heritability estimates confirm the possibility of selecting animals for the traits studied.

Heritability and genetic trend of body weight in dogs of different breeds in Sweden.

High body weight (BW) in dogs has been associated with developmental as well as degenerative diseases, but the heritability of BW in dog breeds is largely unknown. The aim of the current study was to estimate heritability and genetic change (genetic trend) for BW in a range of dog breeds in Sweden. Body weight registrations from 19 dog breeds (with n ranging from 412 to 4,710) of varying body size, type and usage were collected from 2007 to 2016. The average BW of the breeds was 8 to 56 kg. The BW registrations were performed when the dogs were 12 to 24 mo of age (18 to 30 mo for one large-sized breed) in connection with an official radiographic screening program for hip dysplasia. Collected weight records were used to estimate heritability and genetic trends for BW. Several statistical models were used. The preliminary model included the fixed effects of breed (P < 0.001), sex (P < 0.001), year of screening (P < 0.001), litter size (P = 0.06), parity of the dam (P = 0.03) and linear regression on age at screening (P < 0.001), the latter five effects all nested within breed, and the random effects of litter and dam. Season of birth and the quadratic effect of age were also tested, but were not significant (P > 0.10). For the genetic analysis, various mixed linear models were tested within breed with different combinations of random effects; the most complex model included random effects of litter, direct additive, and maternal genetic effects, and maternal permanent environmental effects. The average heritability for BW over all 19 breeds was 51%, with a range of 35% to 70%, and the additive genetic coefficient of variance was around 9%. Maternal heritability was 5% to 9% and litter variance was below 10% with one exception (15% in Shetland Sheepdogs). For nine breeds, there was a genetic trend of increasing BW, whereas seven breeds had a genetic trend of decreasing BW. The largest absolute genetic change over a 10-yr period was around 0.6 kg or about 2% of the mean. In conclusion, given the small genetic changes in spite of the high heritability, it seems that there is generally a very weak selection, if any, for BW in the included dog breeds.

Is there sufficient genetic variation to breed Elsenburg Merino sheep with shorter tails?

Context Breech strike compromises wool production and welfare of Merinos. Long tails contribute to the formation of dags, increasing the risk of sheep attracting gravid blowfly females. Tail-docking is popular globally, as it reduces the incidence of dags. Breeding for a reduced tail length is a more socially accepted measure to replace tail-docking as a management strategy, since stronger legislation for animal welfare is expected. Aim The study used historic data on tail length (TL), birth weight (BW) and bodyweight at docking (DW) to estimate genetic and environmental parameters for these traits in a well known South African resource flock. Methods Merino lambs born from 2016 to 2021 (except for 2020 due to Covid-19) of the Elsenburg Merino flock were recorded for TL, BW and DW. The flock was separated by divergent selection for and against a number of lambs weaned per ewe mated in a High (H) and a Low (L) line. Fixed effects included birth year, sex, selection line, dam age and birth type. ASReml was used to analyse the fixed effects so as to obtain an operational model, before adding the random direct genetic (h2), maternal genetic (m2) and maternal permanent environmental (c2) effects. Results H-line lambs were heavier at birth and tail-docking, with longer tails than those of L-line contemporaries (all P &lt; 0.01). The line difference in TL seemed to be size-dependent, as it was eliminated by adding DW as a covariate to the analysis. The same trend was observed for sex, dam age and birth type. TL was thus affected (P &lt; 0.01) only by birth year in the latter analysis. Regressions of TL on age at tail-docking and DW were highly significant (P &lt; 0.01). Single-trait estimates of h2 were 0.26 for BW, 0.06 for DW, and 0.30 for TL. TL remained heritable at 0.38 in the analysis including DW as a covariate. Including m2 improved the random-effects model for TL (0.08) and DW (0.20). TL and DW were genetically correlated (rG = 0.47 and rM = 0.70). Conclusions TL was moderately heritable, with small m2 and non-significant c2 effects. It will be possible to directly select for TL. However, selection for shorter tails will reduce size if breeding values are not adjusted for DW as a covariate. Implications Further studies on the genetic basis of TL as well as its genetic correlations with other traits of economic importance are warranted.

Multiple‐trait and structural equation modelling approaches to infer genetic relationships between tail length and weight traits in Merinoland sheep

Tail docking is routinely conducted in long-tailed sheep breeds to prevent flystrike infections, but it is not in agreement with legal guidelines and animal welfare issues. Selection on short tails is a sustainable alternative in this regard, but side effects on other breeding goal traits are unclear. In consequence, the present study aimed to estimate genetic parameters for tail length (TL) at birth, birth weight (BW), weaning weight (WW) and postweaning weight (PWW) at the slaughtering date considering single-trait (STM), multiple-trait (MTM) and structural equation models (SEM) with different random effects, and accordingly, different covariance structures. The SEM considered time-lagged recursive relationships among response variables in three different pathways. The first path pertained to the effect of TL on WW and of WW on PWW. The second path reflected the effect of BW on WW and of WW on PWW. The third path was the recursive effect of TL on PWW. The phenotypic data consisted of 2803 records for TL, 13,042 records for BW, 1556 records for WW and 3986 records for PWW from Merinoland lambs. Lambs were born in the period from 1995 to 2021 and kept at the university Gießen research station, Germany, with their naturally long tails. Genetic statistical model evaluation based on Bayesian and Akaike's information criteria suggested models simultaneously considering direct genetic, maternal genetic and maternal permanent environmental effects and respective covariances. For statistical models including the same random effects and covariance structures, SEM were superior over MTM. The direct heritability for TL from the best-fitting STM was 0.60 ± 0.08, indicating the potential for genetic reduction of tail length within a few generations. For growth traits, the direct heritabilities ranged from 0.16 ± 0.03 for BW to 0.31 ± 0.09 for PWW. The maternal heritabilities were 0.03 ± 0.03 for TL, 0.12 ± 0.02 for BW, 0.04 ± 0.03 for WW and 0.07 ± 0.03 for PWW, reflecting small, but the non-significant influence of uterine characteristics on the tail development. The direct genetic correlations between TL and all weight traits were positive and very similar to MTM and SEM but reflected antagonistic genetic relationships from a breeding perspective. Oppositely, the structural equation coefficients reflecting trait associations phenotypically were negative (favourable) for the time-lagged effects of TL on WW and on PWW. As an explanation, lambs with long and woolly tails have an increased risk for contamination with dirt and dust causing infections, which in turn impairs the body weight development. In conclusion, breeding on short tails should consider trait-associated environmental risk factors, for example, disease susceptibility, which can be mimicked via SEM approaches.