Genetic evaluation of Australian dairy cattle for somatic cell scores using multi-trait random regression test-day model

Summary A multi-trait (MT) random regression (RR) test day (TD) model has been developed for genetic evaluation of somatic cell scores for Australian dairy cattle, where first, second and third lactations were considered as three different but correlated traits. The model includes herd-test-day, year-season, age at calving, heterosis and lactation curves modelled with Legendre polynomials as fixed effects, and random genetic and permanent environmental effects modelled with Legendre polynomials. Residual variance varied across the lactation trajectory. The genetic parameters were estimated using asreml . The heritability estimates ranged from 0.05 to 0.16. The genetic correlations between lactations and between test days within lactations were consistent with most of the published results. Preconditioned conjugate gradient algorithm with iteration on data was implemented for solving the system of equations. For reliability approximation, the method of Tier and Meyer was used. The genetic evaluation system was validated with Interbull validation method III by comparing proofs from a complete evaluation with those from an evaluation based on a data set excluding the most recent 4 years. The genetic trend estimate was in the allowed range and correlations between the two sets of proofs were very high. Additionally, the RR model was compared to the previous test day model. The correlations of proofs between both models were high (0.97) for bulls with high reliabilities. The correlations of bulls decreased with increasing incompleteness of daughter performance information. The correlations between the breeding values from two consecutive runs were high ranging from 0.97 to 0.99. The MT RR TD model was able to make effective use of available information on young bulls and cows, and could offer an opportunity to breeders to utilize estimated breeding values for first and later lactations.     

Genetic analysis of the rates of conception using a longitudinal threshold model with random regression in dairy crossbreeding within a tropical environment

This study was designed to: (i) estimate genetic parameters and breeding values for conception rates (CR) using the repeatability threshold model (RP‐THM) and random regression threshold models (RR‐THM); and (ii) compare covariance functions for modeling the additive genetic (AG) and permanent environmental (PE) effects in the RR‐THM. The CR was defined as the outcome of an insemination. A data set of 130 592 first‐lactation insemination records of 55 789 Thai dairy cows, calving between 1996 and 2011, was used in the analyses. All models included fixed effects of year × month of insemination, breed × day in milk to insemination class and age at calving. The random effects consisted of herd × year interaction, service sire, PE, AG and residual. Variance components were estimated using a Bayesian method via Gibbs sampling. Heritability estimates of CR ranged from 0.032 to 0.067, 0.037 to 0.165 and 0.045 to 0.218 for RR‐THM with the second, third and fourth‐order of Legendre polynomials, respectively. The heritability estimated from RP‐THM was 0.056. Model comparisons based on goodness of fit, predictive abilities, predicted service results of animal, and pattern of genetic parameter estimates, indicated that the model which fit the desired outcome of insemination was the RR‐THM with two regression coefficients.     

Genetic characteristics of Japanese Holstein cows based on multiple-lactation random regression test-day animal models

The first three lactation curves of the Japanese Holstein cows were analyzed using a random regression (RR) test-day model with a cubic Legendre polynomial fitted to each of the three parities. The first three eigenvectors of the additive genetic RR covariance matrix explained 77.8, 10.9, and 4.2% of the total variance of the three parities and are associated mainly with the level of milk yield, the linear increase, and the concave curve, respectively. On a lactational basis, as the parity increases, the contribution of the first eigenvector to a lactational variation decreases whereas the contribution of the second eigenvector increases sharply. This means that the impact of the first eigenvector on the level of milk production decreases across parity whereas the effect of the second eigenvector on the shape of the lactation curve increases across parity. The first lactation curve was the most persistent, followed by the second and the third lactation. Persistency and days to reach peak yield decrease as the parity increases (45, 40, and 36 days for the first three parities). Daily heritabilities within lactation were lower for the first parity than for the second or the third parity. The first three lactation curves possess distinctive genetic characteristics that merit consideration when combining the proofs of the first three lactations to select for lifetime production. Within- and between-parity genetic correlations between the constant and the linear RR coefficients were all positive, suggesting that raising the level of milk production in one parity would increase the linear slope in all parities, thus improving persistency. Within- and between-parity genetic correlations between the constant and the quadratic RR coefficients were all negative, implying that increasing the level of production in one parity would deepen and/or widen the concave curve in all parities, thus decreasing persistency. The linear and quadratic RR coefficients were negatively correlated within or between parities and thus have antagonistic effects on persistency.     

Somatic cell score genetic parameter estimates of dairy cattle in Portugal using fractional polynomials

Milk somatic cell count is an indicator trait for mastitis resistance. Genetic parameters for somatic cell score in the Portuguese Holstein-Friesian population were estimated by modeling the pattern of genetic correlation over the first 3 lactations (days in milk) with a random regression model. Data records from the first 3 lactations were from the national database of the Portuguese Holstein Association herds. Heritability estimates ranged from 0.05 at the beginning of the lactation for the 3 lactations, to 0.07 at the end of the lactation period for the first and third lactations, to 0.09 for the second lactation. This increase in the heritability values was due to an increase in the genetic variance and a decrease in the residual variances. Genetic correlations evaluated for monthly time points were high (0.65 to 0.99) for all 3 lactations, whereas phenotypic correlations were much less than the genetic correlations (0.13 to 0.62).     

Random regression models to estimate genetic parameters for test‐day milk yield in Brazilian Murrah buffaloes

The objective of this work was to estimate covariance functions for additive genetic and permanent environmental effects and, subsequently, to obtain genetic parameters for buffalo’s test‐day milk production using random regression models on Legendre polynomials (LPs). A total of 17 935 test‐day milk yield (TDMY) from 1433 first lactations of Murrah buffaloes, calving from 1985 to 2005 and belonging to 12 herds located in São Paulo state, Brazil, were analysed. Contemporary groups (CGs) were defined by herd, year and month of milk test. Residual variances were modelled through variance functions, from second to fourth order and also by a step function with 1, 4, 6, 22 and 42 classes. The model of analyses included the fixed effect of CGs, number of milking, age of cow at calving as a covariable (linear and quadratic) and the mean trend of the population. As random effects were included the additive genetic and permanent environmental effects. The additive genetic and permanent environmental random effects were modelled by LP of days in milk from quadratic to seventh degree polynomial functions. The model with additive genetic and animal permanent environmental effects adjusted by quintic and sixth order LP, respectively, and residual variance modelled through a step function with six classes was the most adequate model to describe the covariance structure of the data. Heritability estimates decreased from 0.44 (first week) to 0.18 (fourth week). Unexpected negative genetic correlation estimates were obtained between TDMY records at first weeks with records from middle to the end of lactation, being the values varied from ?0.07 (second with eighth week) to ?0.34 (1st with 42nd week). TDMY heritability estimates were moderate in the course of the lactation, suggesting that this trait could be applied as selection criteria in milking buffaloes.     

Comparison of variance components for test-day electrical conductivity of milk and test-day somatic cell score for first lactation cows in an experimental herd

In recent decades, electrical conductivity (EC) has been introduced as an indicator of mastitis, and genetic selection based on this trait may be possible. In this study, genetic parameters for test-day EC and test-day somatic cell score (SCS) were compared. Data were collected from a Danish experimental herd, including daily records of EC and SCS from 265 first lactation cows. Different genetic models were tested, and a random regression animal model with a 4th order Legendre polynomial for the permanent environmental effect for both traits, a 1st order Legendre polynomial for the additive genetic effect of EC and a 2nd order Legendre polynomial for the additive genetic effect of SCS, gave the best fit. The patterns of the curves were similar for both permanent environmental and additive genetic variance for the two traits. Heritability estimates ranged from 0.05 to 0.12, and from 0.01 to 0.09, for EC and SCS, respectively. The estimate of genetic correlation between the traits was high, and ranged from 0.86 to 0.98. Based on these results, EC could be a potential indicator trait in a breeding programme where selection for increased mastitis resistance is included.     

Genetic evaluation of growth of Kenya Boran cattle using random regression models

Data consisting of 18 884 weight records collected from 1273 Boran cattle from birth to 24 months of age were used to estimate covariance functions and genetic parameters for growth of Boran cattle using random regression (RR) models under a situation of small herd size and inconsistent recording. The RR model fitted quadratic Legendre polynomials of age at recording for additive genetic and permanent environmental effects. Genetic variance increased from birth, reaching an asymptotic value at 455 days and was maximum at 525 days of age after which it gradually dropped. Permanent environmental variance increased throughout the trajectory. Estimates of temporary environmental variance were heterogeneous across ages. Direct heritability and permanent environmental variance as a proportion of phenotypic variance fluctuated greatly during the early ages but later stabilized at intermediate to later ages; the estimates ranged from 0.11 to 0.33 and from 0.18 to 0.83, respectively. Genetic correlation estimates were positive, ranging from 0.10 to unity. The estimates declined with increasing in lag between the age points. Phenotypic correlation pattern was erratic between early ages, negatively low (-0.02) between the extreme data points and moderate to highly positive (>0.50) between intermediate and later points, with prominent spikes along the diagonal. It is concluded that RR models have potential for modelling growth of Boran cattle, notwithstanding conditions of small herd sizes and inconsistent recording.     

Selection of locations of knots for linear splines in random regression test‐day models

Using spline functions (segmented polynomials) in regression models requires the knowledge of the location of the knots. Knots are the points at which independent linear segments are connected. Optimal positions of knots for linear splines of different orders were determined in this study for different scenarios, using existing estimates of covariance functions and an optimization algorithm. The traits considered were test‐day milk, fat and protein yields, and somatic cell score (SCS) in the first three lactations of Canadian Holsteins. Two ranges of days in milk (from 5 to 305 and from 5 to 365) were taken into account. In addition, four different populations of Holstein cows, from Australia, Canada, Italy and New Zealand, were examined with respect to first lactation (305 days) milk only. The estimates of genetic and permanent environmental covariance functions were based on single‐ and multiple‐trait test‐day models, with Legendre polynomials of order 4 as random regressions. A differential evolution algorithm was applied to find the best location of knots for splines of orders 4 to 7 and the criterion for optimization was the goodness‐of‐fit of the spline covariance function. Results indicated that the optimal position of knots for linear splines differed between genetic and permanent environmental effects, as well as between traits and lactations. Different populations also exhibited different patterns of optimal knot locations. With linear splines, different positions of knots should therefore be used for different effects and traits in random regression test‐day models when analysing milk production traits.     

Genetic analysis of fat‐to‐protein ratio,milk yield and somatic cell score of Holstein cows in Japan in the first three lactations by using a random regression model

We estimated the genetic parameters of fat‐to‐protein ratio (FPR) and the genetic correlations between FPR and milk yield or somatic cell score in the first three lactations in dairy cows. Data included 3 079 517 test‐day records of 201 138 Holstein cows in Japan from 2006 to 2011. Genetic parameters were estimated with a multiple‐trait random regression model in which the records within and between parities were treated as separate traits. The phenotypic values of FPR increased soon after parturition and peaked at 10 to 20 days in milk, then decreased slowly in mid‐ and late lactation. Heritability estimates for FPR yielded moderate values. Genetic correlations of FPR among parities were low in early lactation. Genetic correlations between FPR and milk yield were positive and low in early lactation, but only in the first lactation. Genetic correlations between FPR and somatic cell score were positive in early lactation and decreased to become negative in mid‐ to late lactation. By using these results for genetic evaluation it should be possible to improve energy balance in dairy cows.     

Genetic associations between milk fat‐to‐protein ratio,milk production and fertility in the first two lactations of Thai Holsteins dairy cattle

The aims of this study were to estimate, simultaneously, the genetic parameters of test‐day milk fat‐to‐protein ratio (FPR), test‐day milk yield (MY), and days‐open (DO) in the first two lactations of Thai Holsteins. A total of 76 194 test‐day production records collected from 8874 cows with 8674 DO records between 2001 and 2011 from different lactations were treated as separated traits. The estimates of heritability for test‐day FPR in the first lactation showed an increasing trend, whereas the estimates in the second lactation showed a U‐shape trend. Genetic correlations for FPR‐DO and MY‐DO showed a decreasing trend along days in milk (DIM) in both lactations, whereas genetic correlations for FPR‐MY increased along DIM in the first lactation but decreased in the second lactation. Genetic correlations of FPR between consecutive DIM were moderate to high, which showed the effectiveness of simultaneous analyses. Selection of FPR in the early stage has no adverse effect on MY and DO for the first lactation but has a negative effect on MY and positive effect on DO for the second lactation. This study showed that genetic improvement of the energy balance using FPR, MY and DO with multi‐trait test day model could be applied in a Thailand dairy cattle breeding program.     

Genetic parameters and single versus multi-trait evaluation of udder health traits

Abstract

Genetic parameters were estimated for lactation average somatic cell score (SCS) and clinical mastitis (CM) for the first three lactations of multiparous Finnish Ayrshire cows. A multi-trait linear sire model was used for estimation of covariance components, and the efficiencies of single- versus multi-trait multi-lactation (MT) sire evaluations were compared. Heritability of SCS and CM in the first three lactations ranged from 0.11 to 0.13 and 0.02 to 0.03, respectively. Within lactation, genetic correlations between SCS and CM ranged from 0.68 to 0.72. Within both traits, across-lactation genetic correlations were lowest between 1 and 3, and highest between 2 and 3, with estimates ranging from 0.75 to 0.86 and from 0.81 to 0.98 for CM and SCS, respectively. Residual and phenotypic correlations were low and ranged from 0.09 to 0.13 and from 0.10 to 0.13, respectively. The absolute difference between genetic and residual correlations was from 0.5 to 0.6. Within-lactation genetic correlations between traits that are much less than unity suggest a multi-trait model for genetic evaluation of mastitis resistance. Comparison of model prediction performance between single-trait (ST) and MT models using a data splitting method showed that the MT model was more stable in predicting breeding values in future records of animals. Especially, for young sires and CM, the SD of EBVs from the MT model was 14 to 23% higher than the ST model, indicating more effective use of information in terms of revealing more genetic variation.     

Genetic parameters for dairy production of Australian Black and White cowsParamètres génetiques de la production laitière des vaches Pie Noires AustraliennesGenetische Parameter für die Milchleistung von australischen Schwarzbunten Kühen

An initial study on the inheritance of first and second lactation dairy production in Australian Black and White cows is presented for the states of New South Wales, Victoria, Queensland and Tasmania. A total of 116,043 first and 14,767 second lactation records for 326 young and 415 old, and 200 young and 31 old sires, respectively, were analyzed using a multivariate Restricted Maximum Likelihood procedure. Analyses were carried out for a mixed model fitting herd-year-seasons and proven sires as fixed, young sires as random effects and age at calving as a linear and quadratic covariable. In addition, the effects of lactation length and month of calving were investigated.The within-HYS phenotypic variation was high with distinct differences between states. As a consequence, heritability estimates were low compared to literature values. Pooled estimates were 0.17, 0.15, 0.38, 0.13 and 0.25 for first lactation milk yield, fat yield, fat content, protein yield and protein content, respectively. Correlations among yields were high, ranging from 0.71 to 0.91 genetically, and from 0.90 to 0.95 phenotypically. Milk yield was negatively related to concentration of constituents, genetic and phenotypic correlations being of the order of ?0.4 and ?0.2, respectively.Heritabilities for second lactation milk yield, fat yield and fat content were 0.18, 0.17 and 0.45, respectively, of similiar magnitude to first lactation values. The respective genetic correlations between first and second lactation were 0.96, 0.98 and 0.98, suggesting that production in both lactations is genetically identical. These high estimates can be attributed to the method of analysis, which accounts for culling based on first lactation performance.     

Modeling lactation curves and estimation of genetic parameters in Holstein cows using multiple‐trait random regression models

We compared the goodness of fit of three mathematical functions (including: Legendre polynomials, Lidauer‐Mäntysaari function and Wilmink function) for describing the lactation curve of primiparous Iranian Holstein cows by using multiple‐trait random regression models (MT‐RRM). Lactational submodels provided the largest daily additive genetic (AG) and permanent environmental (PE) variance estimates at the end and at the onset of lactation, respectively, as well as low genetic correlations between peripheral test‐day records. For all models, heritability estimates were highest at the end of lactation (245 to 305 days) and ranged from 0.05 to 0.26, 0.03 to 0.12 and 0.04 to 0.24 for milk, fat and protein yields, respectively. Generally, the genetic correlations between traits depend on how far apart they are or whether they are on the same day in any two traits. On average, genetic correlations between milk and fat were the lowest and those between fat and protein were intermediate, while those between milk and protein were the highest. Results from all criteria (Akaike's and Schwarz's Bayesian information criterion, and ?2*logarithm of the likelihood function) suggested that a model with 2 and 5 coefficients of Legendre polynomials for AG and PE effects, respectively, was the most adequate for fitting the data.     

Dual-purpose Guzerá cattle exhibit high dairy performance under heat stress

The present study evaluated the heat stress response pattern of dual-purpose Guzerá cattle for test-day (TD) milk yield records of first lactation and estimated genetic parameters and trends related to heat stress. A total of 31,435 TD records from 4,486 first lactations of Guzerá cows, collected between 1986 and 2012, were analysed. Two random regression models considered days in milk (DIM) and/or temperature × humidity-dependent (THI) covariate. Impacts of −0.037, −0.019 and −0.006 kg/day/THI for initial and intermediate stages of lactation were observed when considering the mean maximum daily temperature and humidity to calculate THI. Heritability estimates ranged from 0.16 to 0.35 throughout lactation and THI values, suggesting the possibility to expect gains from selection for such trait. The variable trajectory of breeding values for dual-purpose Guzerá sires in response to changes in THI values confirms that the genotype × environment interaction due to heat stress can have some effect on TD milk yield. Despite the high dairy performance of Guzerá cattle under heat stress, estimated genetic trends showed a progressive reduction in heat tolerance. Therefore, new strategies should be adopted to prevent negative impacts of heat stress over milk production in Guzerá animals in future.     

Modeling final scores in US Holsteins as a function of year of classification using a random regression model

Conformation final scores in Holsteins were used to assess genetic changes over the years due to various factors such as selection and changes in trait definition. The model included management group, age group, and stage of lactation as fixed effects; additive genetic effects with random regressions on year of classification using Legendre polynomials with order from linear to cubic; and residual effects assuming heterogeneous variances. Two sets of simulated data were used to test the feasibility of variance component estimation in situations where the definition of the trait of interest changes continuously over time. Estimated variances from the simulated data sets were unbiased. Empirical tests involved 30,041 records of cows with single records scored in 1981-1999. Heritability estimates and additive genetic variances from field data decreased while residual variances increased over time. Differences among estimates of variance components from linear, quadratic and cubic random-regression models were small. Genetic correlations among final scores at years of classification estimated with the multiple-trait model that treated different groups of years as separate traits and with linear, quadratic and cubic random-regression models decreased from 1.0 to a minimum of 0.91, as the distance between the years increased. Although there were no significant differences among estimates of variance components from random-regression models, genetic correlations between different years estimated with higher order random-regression models were closer to those with the multiple trait model that treated different group of years as separate traits. Genetic changes in a trait over time can be studied with a random-regression model.     

Application of robust procedures for estimation of breeding values in multiple-trait random regression test-day model

Robust procedures for estimation of breeding values were applied to multiple‐trait random regression test‐day (TD) model to reduce the influence of outliers on inferences. Robust estimation methods consisted of correcting selected observations (defined as outliers) in the process of solving mixed‐model equations in such a way that ‘new’ observations gave residuals (actual observation minus predicted) within k residual standard deviations for a given day in milk in 305‐day lactation. Data were 980 503 TD records on 63 346 Canadian Jersey cows. Milk, fat, protein and somatic cell score in the first three lactations were analysed jointly in the model that included fixed herd‐TD effect and regressions within region–age–season of calving, and regressions with random coefficients for animal genetic and permanent environmental effects. All regressions were orthogonal polynomials of order 4. Robust procedures for k = 1.5, 2.0, 2.5, 2.75 and 3.0 were contrasted with the regular best linear unbiased prediction (BLUP) method in terms of numbers and distributions of outliers, and estimated breeding values (EBV) of animals. Distributions of outliers were similar across traits and lactations. Early days in milk (from 5 to 15) were associated with larger frequency of outliers compared with the remaining part of lactation. Several, computationally simple, robust methods (for k > 2.0) reduced the influence of outlier observations in the model and improved the overall model performance. Differences in rankings of animals from robust evaluations were small compared with the regular BLUP method. No clear associations between changes in EBV (rankings) of top animals from different methods and the occurrence of outliers were detected.     

Incidence and economics of clinical mastitis in five Holstein herds in the Czech Republic

Data on clinical mastitis (CM) collected between 1996 and 2003 on five Holstein dairy farms in the Czech Republic were analyzed. Lactational incidences of CM, averaged across farms and calculated only from cows with complete lactations, were 0.35, 0.45 and 0.57 for the first, second and third plus subsequent lactations, respectively. The mean numbers of CM cases per cow and lactation were 0.63, 0.94 and 1.22, and the incidence of CM cases per cow-year at risk were 0.68, 1.00 and 1.27 for the first, second and third plus subsequent lactations, respectively. Longitudinal analysis of CM prevalence based on daily records showed the highest proportion of infected cows in the first 10 days of lactation. The within-farm incidence of CM cases per cow per year, averaged over lactations, ranged from 0.53 to 1.56 with a mean value of 0.94 in the whole data set. Direct financial losses from CM per cow per year within farm ranged from 43.63 to 84.84 euros. They included losses from discarded milk, cost for drugs, veterinary service, herdsman's time, cost for an extra milking machine and cost for antibiotic drying of cows. The economic value of CM incidence (change in direct losses per cow per year when increasing CM incidence by one case above the average value) ranged from 58.3 to 80.1 euros per CM case per cow per year with the mean value of 62.6 euros per CM case per cow per year in the total data set. Daily prevalence rate of CM was shown to be the best among various indicators of CM susceptibility, because it accounted for the censored character of the data and for repeated cases of CM within lactations. In order to reduce the incidence of clinical mastitis for dairy cattle in the Czech Republic, we recommend that it should be included as a goal in the breeding program.     

Accessing genotype by environment interaction using within- and across-country test-day random regression sire models

First-lactation test-day (TD) milk records of Luxembourg and Tunisian Holsteins were analysed for evidence of genotype by environment interaction (G × E). The joint data included 730 810 TD records of 87 734 cows and 231 common sires. Random regression TD sire models with fourth-order Legendre polynomials were used to estimate genetic parameters via within- and across-country analyses. Daily heritability estimates of milk yield from within-country analysis were between 0.11 and 0.32, and 0.03 and 0.13 in Luxembourg and Tunisia, respectively. Heritability estimates for 305-day milk yield and persistency (defined as the breeding value for milk yield on DIM 280 minus the breeding value on DIM 80) were lower for Tunisian Holsteins compared with the Luxembourg population. Specifically, heritability for 305-day milk yield was 0.16 for within- and 0.11 for across-country analyses for Tunisian Holsteins and 0.38 for within- and 0.40 for across-country analyses for Luxembourg Holsteins. Heritability for apparent persistency was 0.02 for both within- and across-country analyses for Tunisian Holsteins and 0.08 for within- and 0.09 for across-country analyses for Luxembourg Holsteins. Genetic correlations between the two countries were 0.50 for 305-day milk yield and 0.43 for apparent persistency. Moreover, rank correlations between the estimated breeding values of common sires for 305-day milk yield and persistency, estimated separately in each country, were low. Low genetic correlations are evidence for G × E for milk yield production while low rank correlations suggest different rankings of sires in both environments. Results from this study indicate that milk production of daughters of the same sires depends greatly on the production environment and that importing high merit semen for limited input systems might not be an effective strategy to improve milk production.     

A comparison of different measures of persistency with special respect to variation of test-day milk yields

Six measures of persistency of milk yield were compared by estimation of heritabilities, genetic correlations and the amount of concentrates required by cows with high and low persistency.Persistency was expressed as ratio between different parts of the lactation (P2:1 and P3:1), as a ratio between maximum test-day milk yield and mean test-day milk yield and as the standard deviation of the test-day milk yields of a lactation. The investigation was based on 39 349 first, 23 910 second and 13 651 third lactation records of Simmental cows from the region of Lower Austria.The heritability estimates ranged from 0.12 to 0.18 for measurements including the first 200 days of lactation and from 0.17 to 0.22 when the whole 305-day lactation was included. The largest values were found for the standard deviation of test-day milk yields (305 days); 0.21, 0.22 and 0.22 for the first, second and third lactation, respectively. Genetic correlations between lactations were high for all measures of persistency, ranging from 0.79 to 0.95. Highly persistent cows required between 69 and 161 kg less concentrate than cows with a poor persistency to produce 5500 kg of milk. The difference was generally larger when the grouping was performed on measures including 305 days of lactation vs. measures including only earlier parts of the lactation; 161 kg was found for P3:1.     

Genetic evaluation of growth in Barki sheep using random regression models

The objective of the current study was to estimate covariance components of growth at different ages from birth to yearling in Barki lambs. A total of 16,496 records for body weights at birth (W0), 3 (W3), 6 (W6), 9 (W9), and 12 (12) months of age for Barki lambs were available. Two statistical approaches were used; multi-trait (MT) and random regression (RR) animal models assuming two random effects only, additive genetic effect (σ²_a) and permanent environmental effect (σ²_pe) of the animal. Regarding the RR model, Legendre polynomials (LP) of different orders for the random parts were compared in order to evaluate the most appropriate model. Bayesian information and Akaike information criteria suggested that the optimal RR model included the third order for fixed effect of lamb age and σ²_pe, and fourth order of LP for σ²_a (LP₃₄₃). Estimates of direct heritability (h²_a) from LP₃₄₃ showed an ascending pattern, as it was 0.06 ± 0.03 for birth weight and reached to the peak at 9 months (0.42 ± 0.02). Thereafter, it declined again at the end of trajectory (12 months of age; 0.27 ± 0.03). The MT model showed a fluctuated pattern and lower estimates of h²_a (0.19 ± 0.03, 0.11 ± 0.02, 0.12 ± 0.02, 0.11 ± 0.03, and 0.16 ± 0.04 for W0, W3, W6, W9, and W12, respectively). Considerably, similar ascending patterns of the ratio of σ²_pe to phenotypic variance were reported from both RR (from 3 to 50%) and MT models (from 5 to 20%). Of interest, the RR model showed higher predicting ability of the breeding values compared with the MT model, which is an indicator for the suitability of RR models for analyzing the consecutive growth traits in sheep. Results suggested that the Barki sheep has a potential for genetic selection based on weight at different ages with selection likely to be more efficient at 9 months of age.