Permanent Environmental Effects Research Articles

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.

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 > 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.

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 the 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. Additionaly, 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. Aferward, we selected the model with 5 orthogonal polynomials based on the Akaike Information Criteria (AIC). The Restricted Maximum Likelihood (REML) 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, occassionally 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.

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 indicate the possibility of genetic gain by selecting for reproductive traits of females from a commercial tilapia population (Oreochromis niloticus)

This study aimed to verify the possibility of obtaining genetic gain by selecting breeders based on genetic parameter estimates for reproductive performance traits of female tilapia (Oreochromis sp.) from a commercial population. The study was conducted using a database containing information on reproductive traits collected from 2009 to 2021. During the breeding period, the animals were stocked in tanks in a 1:3 male: female ratio. Eggs were collected from the mouth of the females every five days, and the collected eggs were measured in 100 mL beakers to estimate the egg volume. To estimate the variance components and genetic parameters (heritability, correlation, and repeatability), a mixed animal model was used with the fixed effects of the production cycle and breeding tanks, in addition to the additive direct genetic and permanent environmental random effects. The heritability values were 0.26 ± 0.14, 0.18 ± 0.10, 0.15 ± 0.09, and 0.13 ± 0.08 for total egg volume, average egg volume, spawning frequency, and average spawning interval, respectively, with repeatability above 0.43 for all analyzed variables, demonstrating the ability of the evaluated breeds to maintain the same production level during the different cycles. The genetic correlations among the variables were high, with a correlation of −0.99 between the total egg volume and the average spawning interval. Thus, selecting for total egg volume, which had the highest heritability, would result in animals that produce a large volume of eggs and have short spawning intervals.

Spatio-temporal modeling of high-throughput multispectral aerial images improves agronomic trait genomic prediction in hybrid maize.

Design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments, but precisely measuring spatial heterogeneity in the field remains a challenge. To this end, our study evaluated approaches to improve spatial modeling using high-throughput phenotypes (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index was measured by a multispectral MicaSense camera and processed using ImageBreed. Contrasting to baseline agronomic trait spatial correction and a baseline multitrait model, a two-stage approach was proposed. Using longitudinal normalized difference vegetation index data, plot level permanent environment effects estimated spatial patterns in the field throughout the growing season. Normalized difference vegetation index permanent environment were separated from additive genetic effects using 2D spline, separable autoregressive models, or random regression models. The Permanent environment were leveraged within agronomic trait genomic best linear unbiased prediction either modeling an empirical covariance for random effects, or by modeling fixed effects as an average of permanent environment across time or split among three growth phases. Modeling approaches were tested using simulation data and Genomes-to-Fields hybrid maize (Zea mays L.) field experiments in 2015, 2017, 2019, and 2020 for grain yield, grain moisture, and ear height. The two-stage approach improved heritability, model fit, and genotypic effect estimation compared to baseline models. Electrical conductance and elevation from a 2019 soil survey significantly improved model fit, while 2D spline permanent environment were most strongly correlated with the soil parameters. Simulation of field effects demonstrated improved specificity for random regression models. In summary, the use of longitudinal normalized difference vegetation index measurements increased experimental accuracy and understanding of field spatio-temporal heterogeneity.

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

Methane (CH4) 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 to establish 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 1st to 5th order Legendre polynomials for the additive genetic and within lactation permanent environmental effect. The mean heritability was 0.17 ± 0.04, and the mean within lactation repeatability was 0.56 ± 0.03. The genetic correlations between days in milk 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 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 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 lower genetic gains than predicted from the reliability.

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.

Estimation of additive and maternal covariance of production traits in Murrah buffalo.

The study was done to determine additive, maternal and common permanent environmental effects and best-suited model for some production traits using six univariate animal models that differed in the (co)variance components fitted to assess the importance of maternal effect using likelihood ratio test in Murrah buffaloes. Data from 614 Murrah buffaloes related to production traits were collected from history pedigree sheets maintained at the buffalo farm, Department of Livestock Production and Management (LPM), LUVAS, Hisar. The production traits under this study were 305 days milk yield (305DMY), peak yield (PY), lactation length (LL), dry period (DP), lactation milk yield (LMY) and wet average (WA). The heritability estimates were in the range of 0.33-0.44 for 305DMY, 0.25-0.51 for PY, 0.05-0.13 for LL, 0.03-0.23 for DP, 0.17-0.40 for LMY and 0.37-0.66 for WA. Model 1 was considered best for most of the traits, viz., 305DMY, PY, LL, LMY and WA followed by model 2 for DP. Covariance and correlated values within the traits caused inflation of heritability in model 3 and model 6. The maximum covariance between the additive and maternal effect was found in trait LMY, which was 14,183.90 in model 3 and the minimum value was also reported in the same trait for model 6, valued at -3522.37. Multivariate analysis showed that all production traits were moderate to high and positively correlated with each other except for DP, which was low and negative genetic and phenotypic correlated. Spearman's rank correlation coefficients of breeding value among all six models were high and significant, ranged from 0.78 to 1.00 for all the traits except DP, therefore any of the models could be taken into account depending upon the availability of data.

Modelling the lactation curve in Alpine × Beetal crossbred dairy goats using random regression models fitted with Legendre polynomial and B-spline functions.

The current study sought to genetically assess the lactation curve of Alpine × Beetal crossbred goats through the application of random regression models (RRM). The objective was to estimate genetic parameters of the first lactation test-day milk yield (TDMY) for devising a practical breeding strategy within the nucleus breeding programme. In order to model variations in lactation curves, 25,998 TDMY records were used in this study. For the purpose of estimating genetic parameters, orthogonal Legendre polynomials (LEG) and B-splines (BS) were examined in order to generate suitable and parsimonious models. A single-trait RRM technique was used for the analysis. The average first lactation TDMY was 1.22 ± 0.03 kg and peak yield (1.35 ± 0.02 kg) was achieved around the 7th test day (TD). The present investigation has demonstrated the superiority of the B-spline model for the genetic evaluation of Alpine × Beetal dairy goats. The optimal random regression model was identified as a quadratic B-spline function, characterized by six knots to represent the central trend. This model effectively captured the patterns of additive genetic influences, animal-specific permanent environmental effects (c2 ) and 22 distinct classes of (heterogeneous) residual variance. Additive variances and heritability (h2 ) estimates were lower in the early lactation, however, moderate across most parts of the lactation studied, ranging from 0.09 ± 0.04 to 0.33 ± 0.06. The moderate heritability estimates indicate the potential for selection using favourable combinations of test days throughout the lactation period. It was also observed that a high proportion of total variance was attributed to the animal's permanent environment. Positive genetic correlations were observed for adjacent TDMY values, while the correlations became less pronounced for more distant TDMY values. Considering better fitting of the lactation curve, the use of B-spline functions for genetic evaluation of Alpine × Beetal goats using RRM is recommended.

Genetic parameters for novel mastitis traits defined by combining test-day somatic cell score and differential somatic cell count in the first lactation of Japanese Holsteins

In this study, we aimed to improve current udder health genetic evaluations by addressing the limitations of monthly sampled somatic cell score (SCS) for distinguishing cows with robust innate immunity from those susceptible to chronic infections. The objectives were to (1) establish novel somatic cell traits by integrating SCS and the differential somatic cell count (DSCC), which represents the combined proportion of polymorphonuclear leukocytes and lymphocytes in somatic cells and (2) estimate genetic parameters for the new traits, including their daily heritability and genetic correlations with milk production traits and SCS, using a random regression test-day model (RRTDM). We derived 3 traits, namely ML_SCS_DSCC, SCS_4_DSCC_65_binary, and ML_SCS_DSCC_binary, by using milk loss estimates at corresponding SCS and DSCC levels, thresholds established in previous studies, and a threshold established from milk loss estimates, respectively. Data consisted of test-day records collected during January 2021 through March 2022 from 265 herds in Hokkaido, Japan. From these records, we extracted records between 7 to 305 d in milk (DIM) in the first lactation to fit the RRTDM. The model included the random effect of herd-test-day, the fixed effect of year-month, fixed lactation curves nested with calving age groups, and random regressions with Legendre polynomials of order 3 for additive genetic and permanent environmental effects. The analysis was performed using Gibbs sampling with Gibbsf90+ software. The averages (ranges) of daily heritability estimates over lactation were 0.086 (0.075 to 0.095) for SCS, 0.104 (0.073 to 0.127) for ML_SCS_DSCC, 0.137 (0.014 to 0.297) for SCS_4_DSCC_65_binary, and 0.138 (0.115 to 0.185) for ML_SCS_DSCC_binary; the heritability curve for SCS_4_DSCC_65_binary was erratic. Genetic correlations within the trait decreased as the DIM interval widened, especially for those integrating DSCC, indicating that these traits should be analyzed using RRTDM rather than repeatability models. The averages (ranges) of genetic correlations with milk yield over lactation were 0.01 (-0.22 to 0.28) for SCS, -0.05 (-0.40 to 0.13) for ML_SCS_DSCC, -0.08 (-0.17 to 0.09) for SCS_4_DSCC_65_binary, and -0.08 (-0.22 to 0.27) for ML_SCS_DSCC_binary. Compared with SCS, the newly defined traits exhibited slightly stronger negative genetic correlations with milk yield. Especially in late lactation stages, the genetic correlation between ML_SCS_DSCC and milk yield was significantly below zero, with a posterior median of -0.40. Furthermore, the new traits showed positive correlations with SCS, having estimates varying from 0.68 to 0.85 for ML_SCS_DSCC, 0.14 to 0.47 for SCS_4_DSCC_65_binary, and 0.61 to 0.66 for ML_SCS_DSCC_binary, depending on DIM. Considering that ML_SCS_DSCC and ML_SCS_DSCC_binary have relatively high heritability (compared with SCS) and favorable genetic correlations with milk production traits and SCS, their incorporation into breeding programs appears promising. Nevertheless, their genetic relationships with (sub)clinical mastitis require further investigation.

Estimation of genetic parameters for growth traits and kleiber ratio in dorper sheep breed

The study aimed to estimate genetic and phenotypic parameters for growth traits and Kleiber ratio in Dorper sheep breed. Data used in this study were collected over 12 years (2012–2023) at Debre Birhan Agricultural Research Center sheep research station in Ethiopia. Studied traits were body weights at birth (WT0), weaning (WT3), six month (WT6), and yearling (WT12) age; average daily gains from birth to weaning (ADG0-3), from weaning to six months (ADG3-6), from six months to yearling (ADG6-12); and Kleiber ratios from birth to weaning (KR1) and from weaning to six months (KR2). The (co)variance components were estimated with different animal models using Average Information Restricted Maximum Likelihood (AI-REML) procedure. The best-fitted model for each trait was determined using likelihood ratio tests. Phenotypic performance for WT3, WT6, WT12, ADG0-3 and ADG3-6 showed a decline trend at a rate of 0.216 kg, 0.794 kg, 0.671 kg, 2.601 g and 4.865 g over years respectively. However, WT3, WT6, WT12, ADG0-3 showed a positive genetic improvement trend at a rate of 0.029 kg, 0.043 kg, 0.049 kg and 0.257 g over years respectively. Year of birth had a significant effect (P < 0.001) on all studied traits. Model including direct genetic as well as permanent environmental effect (Model 2) was chosen as the most appropriate model for WT0. Model which included only direct genetic effect (Model 1) was the best-fit model for all other studied traits. Direct heritability estimates based on suitable models were 0.07 ± 0.06, 0.11 ± 0.06, 0.09 ± 0.07, 0.11 ± 0.09 0.11 ± 0.06, 0.00 ± 0.04, 0.15 ± 0.07 and 0.00 ± 0.04 for WT0, WT3, WT6, WT12, ADG0-3, ADG3-6, KR1 and KR2 respectively. The variance ratio for the permanent environmental effect was 0.13 ± 0.04 for WT0. Genetic correlations among the traits ranged from negative (−0.39) for WT0-KR1 to high (0.99) for WT3-ADG0-3 and phenotypic correlations ranged from negative (−0.31) for WT0-KR1 to high (0.98) for WT3-ADG0-3. The low direct heritability estimates for the studied traits indicated that genetic improvement by direct selection might be difficult. Further investigation for the unexpected declined trend of phenotypic performance over years need to be required.

Possibilities of Using “BUGA” Named Software for Breeding Value Estimation of Anatolian Water Buffalo Population of Istanbul

In this study, variance components and breeding value estimations of Anatolian water buffaloes were estimated from the data obtained within the scope of community based water buffalo improvement program of Anatolian water buffalo population of İstanbul. The "BUGA" named software, which was started to be developed with the support of Harran University Scientific Research Project Unit (HUBAP) for use in the estimation of (co)variance components and breeding value were used in this study. The BUGA software used in the research can predict genetic and environmental effects together with variance components as Best Linear Unbiased Prediction (BLUP) with the Restricted Maximum Log likelihood (REML) method. Unlike its counterparts of another software of foreign origin, it has a visual interface. The language of use is Turkish. The dataset includes records such as city, district, village, genealogy information, date of birth, gender, calving date, lactation parity, number of test day milk yield, lactation milk yield and previous calving date of 442 Anatolian buffaloes raised in this population. The records were analysed using the repeatability model with BUGA software and the additive genetic and permanent environmental effects of all animals in the dataset were estimated. Random environment variance, genotypic variance and permanent environment variance values were estimated as 107112.60 kg2, 20045.22 kg2 and 1259.50 kg2, respectively, while heritability (h2) and degree of repeatability (r) were determined as 0.1561 and 0.1659, respectively. The results obtained as an example of case study applied for the possibility of usage the BUGA software in the estimation of genetic parameters and breeding values showed that BUGA software can be easily used in farm recording conditions with the advantage of friendly user features.

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.

Relationship between Somatic Cell Score and Fat Plus Protein Yield in the First Three Lactations in Spanish Florida Goats

The aim of this study was to estimate genetic parameters of somatic cell score (SCS) and fat plus protein yield (FPY) using repeatability (RM) and random regression (RRM) models in Florida goats. The data consisted of 340,654 test-day controls of the first three lactations, and the pedigree contained 36,144 animals. Covariance components were estimated with a bivariate RM and RRM using the REML approach. Both models included as fixed effects the combination of herd and control date, litter size, kidding number and lactation length, and as random effects, the additive genetic and permanent environmental effects. A variation in the shape of the genetic parameters along the lactation curve was observed for both traits, and h2 oscillated between 0.272 and 0.279 for SCS and 0.099 and 0.138 for FPY. The genetic correlation between SCS and FPY was negative and medium (−0.304 to −0.477), indicating that a low-SCS EBV is associated with a genetic predisposition to high FPY production. Our results showed that given the magnitude of h2 for SCS and its rg with FPY, the SCS could be used as a selection criterion to increase resistance to mastitis, thus obtaining an improved dairy and cheese aptitude in this breed.

Genetic relationships between reproductive and production traits in Jersey crossbred cattle

The study aimed to estimate the genetic parameters of different reproductive traits namely age at first calving (AFC), calving interval (CI), days open (DO) and number of service per conception (NSPC) and their associations with productive traits including 305-day milk yield (305DMY), total lactation milk yield (TLMY) and lactation length (LL) of Jersey crossbred cattle maintained at Kalyani, Nadia, West Bengal, India. Genetic parameters of reproductive traits and their correlations with productive traits were estimated by Restricted Maximum Likelihood method and Bayesian approach. Using both analytical approaches, the estimates of heritability for AFC, CI, DO and NSPC ranged from 0.12 −0.15, 0.05–0.08, 0.08–0.09 and 0.04–0.06, respectively. Low proportion of variances associated with permanent environmental effect of animals (c2 effect) were detected for CI (0.08–0.10), DO (0.09–0.11) and NSPC (0.05–0.06) in both the methods. Repeatability measures for all the reproductive traits considered in this study were low to moderate in nature, which ranged from 0.09 to 0.17. Genetic correlations between different reproductive traits were positive and low (0.05) to high (0.98) in magnitude except AFC-NSPC. Low and negative genetic correlations of AFC with 305DMY and TLMY were favourable and indicated animals with high milk yield had early age of maturity. Positive genetic correlations between CI, DO and NSPC with all production traits implied the antagonism relationships among these traits, therefore in any breeding program for improvement of production traits via selection, the reproductive traits should be taken into account as well.

PSV-2 Difference in Sire Pta Evaluated By Taiwan Dhi and the Original Countries

Abstract Taiwan is located in a region with a tropical/subtropical oceanic climate characterized by high temperatures and humidity, which significantly affects the milk production, health, and reproduction of dairy cows. Most dairy herds in Taiwan have utilized semen of superior foreign sires from countries in temperate climate, mainly from USA and Canada. However, the performance of daughters in Taiwan of those foreign sires may differ from those in temperate countries. Therefore, the objective of this study was to evaluate the performance of foreign sire semen in Taiwan's dairy herds, and to compare the PTAs of those sires in both Taiwan and their original countries. Data and pedigree from the Dairy Herd Improvement program (DHI) were collected between 2009 and 2021. In total, 108,348 lactation records from 54,269 cows and 76,024 animals in the pedigree were obtained from 120 herds. Traits to evaluate include milk yield (MY), milk fat percentage (MF%), milk fat yield (MFY), milk protein percentage (MP%), and milk protein yield (MPY) standardized to 305d-2X yield. The herd-year-season and lactation were considered as fixed effects in the model with additive genetic effect and permanent environmental effect as random effects. In addition, an extra fixed effect, physical situation of lactation (PSL), was included to account for abnormal physiology or subclinical mastitis in those dairy cows. The ASReml program was used to estimate the variance and covariance components as well as heritability and correlation with multiple-trait animal models. Not only the accuracy of predicted transmitting ability (PTATW) ≥ 75% of those superior foreign sires using DHI data and their PTAs obtained from the original countries (PTAInt) were compared, but also the rank correlation between PTATW and PTAInt was also calculated. The results indicated that only when the accuracy of PTATW increased to 90%, a moderate correlation was observed. Moreover, PTATW and PTAInt of all the sires with an accuracy of PTATW ≥ 75% were tracked to the lactation performance of their daughters in Taiwan using regression analysis. The results showed that selecting sires with an increase of 1 kg in PTATW and in PTAInt would increase the average production of their daughters in Taiwan's dairy herds by 1.620 kg and 0.213 kg in milk yield, 1.328 kg and 1.201 kg in fat yield, and 1.493 kg and 0.550 kg in protein yield, respectively. Therefore, selecting offspring through PTATW of sires is much more efficient, and increasing the number of daughters for each sire and their DHI records would improve the accuracy of PTATW prediction and ranking, making it an important indicator for selecting progeny in Taiwan dairy herds.

142 Genotype-By-Environment Interactions in Dairy Cattle Raised in California and New England

Abstract Genotype-by-environmental interactions (G x E) have been reported to cause sire reranking in dairy populations. However, the effects of such interactions are not included in the current US national dairy genetic evaluation. Phenotypes collected from animals across the country are considered the same trait, disregarding interactions between genetic effects and region or other environmental factors. Variations in production systems may interact with the genotypes of the animals, leading to distinct phenotypes. Separating data into regions could change decision-making on bull selection, which is the most important step of a breeding program. The goal of this study is to evaluate the extent of genotype-by-environment interaction between the state of California and the region of New England in the US Holstein Population. Data from Holstein cows in the aforementioned regions collected from 2010 to 2020 were provided by the Council on Dairy Cattle Breeding. The genetic evaluation models used for the national evaluation were modified to account for New England and California phenotypes as separated yet correlated traits. The mixed model accounted for additive genetic, sire-by-herd interaction, and permanent environmental effects as random, and for the management group, age of parity, birth year, and parity as fixed effects. In some scenarios, sire-by-herd interaction and permanent environment were removed due to convergence problems. Variance components were estimated using AIREML under the Method R procedure. Such a method allows computing variance components for large databases by splitting the data into several random groups. Ten random samples of data containing 100,000 records were used for each analysis. The scenarios included single trait for each state, single trait with both states, and two-trait with separated states. Breeding values from the top 100 bulls in each region were compared with evaluate potential sire reranking. Heritabilities varied across regions for all three traits (Table 1). California presented lower heritability than New England across methods. The genetic correlations on the two-trait analysis averaged at 0.96, 0.92, and 0.95 for yield, fat, and protein, respectively. The Spearman correlation coefficient between breeding values of the bulls with more than 200 daughters was 0.87 for milk yield, suggesting that the reranking is unimportant. The genetic correlations suggested that GxE between the two regions is small and that the traits should not be split in the current genetic evaluation. In conclusion, no critical effect of genotype-by-environment interaction was observed, even though top bulls may differ across regions. The next step of this project is to evaluate GxE for somatic cell score and to include genomic information in the evaluations. Finally, a genome-wide association study across the two regions will be performed to investigate changes in genomic regions associated with the phenotypes.

140 Targhee Sire Benchmarking at the U.S. Sheep Experiment Station

Abstract The Targhee breed was developed at the U.S. Sheep Experiment Station (USSES) starting in 1926, and has become an established range breed used commercially in the Western U.S. Our objective was to benchmark the performance of industry-based (external) Targhee sires with those currently in use at USSES. Nineteen Targhee sires from 12 flocks were incorporated into the USSES breeding flock starting with the 2015 breeding season. Data were measured through 2022 for 3 production and 12 wool traits with a range of 180 to 1,967 records per trait. Externally sourced sires averaged 38.3 progeny (7 to 78), with 0 to 4 ram lambs (15 total) and 0 to 15 (106 total) ewe lambs retained per sire in the flock. They were ancestors of 984 grand-progeny. USSES sires averaged 25.0 progeny (5 to 52), with 0 to 1 ram lambs (2 total) and 0 to 8 (53 total) ewe lambs retained per sire. They had 275 grand-progeny. Pedigree data was used to compute the marginal genetic contributions for each sire to the current flock using ENDOG. External sires had a cumulative contribution of 31.9 percent (0.4 to 3.9) while the USSES sires contributed 1.4 percent (0.0 to 0.9). Using R, fixed effects examined were age of dam, lamb sex, lamb birth year, grazing group, and type of birth/rearing. Significant variation explained in a trait by main effects (P &amp;lt; 0.005), and their two-way interactions (P &amp;lt; 0.001), were included in the final linear mixed models fitted. Externally sourced and USSES sires were assigned to separate genetic groups to account for the anticipated differences in genetic base. Using WOMBAT, a univariate animal model was fitted to estimate variance components and predict estimated breeding values for each lamb trait. Selection of random effects was based on log-likelihood ratio tests. Final models fitted and estimated heritabilities are summarized in Table 1. Total litter weight at 120 days per ewe lambing (TW120) was evaluated using a repeated measures model with direct additive and permanent environmental random effects of the ewe. Genetic group solutions were not significantly different (P &amp;gt; 0.05) for any traits evaluated. Since both sire groups originated from the same population, this suggests significant selection divergence had not occurred between industry and USSES flocks. Incorporation of external Targhee sires increased the genetic diversity of the USSES flock. It also strengthened industry ties to the USSES flock, which will benefit the commercial genetic evaluation conducted by the National Sheep Improvement Program. USDA is an equal opportunity provider and employer.