breeding value

Accurate estimation of genetic and phenotypic variance enhances the selection of superior horses and serves as a valuable tool for the long-term improvement of the sport horse population. Therefore, this study was conducted to estimate the variance components for performance traits of sport horses using the Bayesian method.

A database was created using 49,026 records from 1499 horses collected between 2017 and 2020 from the Iranian Equestrian Federation. The sport performance traits examined were race completion time (RCT), number of errors in competition (NEC), and rank at the end of the competition (REC). Analysis of variance and Duncan's multiple comparison tests were used to determine the significance of environmental effects, and genetic parameters were estimated using a Gibbs sampling method. R software was utilized to evaluate environmental effects, fit the model, and estimate reliability, variance components, heritability, and genetic correlations, with the GIBBS1F90 and THRGIBBS1F90 software used for estimation. The statistical model included fixed effects for birth year, sex, age, breed, height of obstacles, and level of difficulty of the event, as well as random effects for rider, date, city of competition, and additive genetic effect.

Heritability (h2) was estimated using single-trait and multi-trait models for the traits RCT, NEC, and REC, respectively, as 0.02 and 0.08, 0.13 and 0.23, 0.16 and 0.29. The estimated genetic correlations between the traits RCT and NEC, RCT and REC, and NEC and REC were 0.38, 0.36, and 0.65, respectively. The mean estimated reliability (r2) using single-trait and multi-trait models for the traits RCT, NEC, and REC were 0.60 and 0.69, 0.62 and 0.73, 0.58 and 0.66, respectively. The heritability values of different traits can vary, and a specific trait may exhibit different levels of heritability across various populations. The estimated heritability of RCT, NEC, and REC fell within the range of values reported in various horse populations and it was <0.01–0.41, 0.07–0.38, and 0.02–0.23, respectively. These estimates demonstrate genetic variation in the traits within the study population, and their alignment with other studies increases confidence in the estimated values.

The results of the present study indicated that the heritability of the traits studied was low. Among the performance traits, REC showed the highest heritability. Due to its positive genetic correlation with RCT and NEC, selecting for REC could potentially improve the other traits as well. These findings emphasize the importance of using multi-trait models in breeding programs, as they can provide more accurate heritability estimates and enhance the precision and reliability of breeding value predictions.

 Determining the production function is one of the most effective ways to monitor the continuity of milk production, as it indicates the relationship between the intake of feedstuff and milk production. To explain the production function in breeding programs and estimate regression coefficients, third-degree nonlinear regression is used. The milk production curve follows a third-degree rule, and therefore, it can be divided into three basic parts. Second-degree production functions cannot accurately represent a milk production curve from the beginning of lactation to the time of dryness, because they only depict the second-degree performance of milk production from the beginning of lactation to the peak of lactation. The aim of this research was to investigate the effects of the production function in breeding programs and their potential use in selecting superior animals.

 In order to recognize the opportunities for profitability in a herd, we first need to create the right production function. To achieve these goals, data on milk production and feed intake from one of the industrial farms in Tehran province were used. Various methods were examined in this study: 1- using OLS approach in R environment to estimate the milk production function, 2- using the Peykani extended ordinary least square (POLS) method, 3- estimating the breeding value of milk production using POLS function and physical milk production (field data) using Wombat software, 4- comparing OLS production functions obtained in R with the POLS production function, 5- Conducting genetic evaluation of animals and ranking dairy cows. When the production functions were obtained according to the POLS program, the optimal amounts of feed consumption and milk production were calculated. The breeding value of milk production was estimated using a repeatability model with permanent environmental effects that consider covariance between records of an animal and this was done using the Wombat program. Finally, cows were ranked based on their genetic rank.

The estimated functions based on the ordinary least squares method were incorrect in terms of signs and coefficients, and did not fit the milk production curve well. Based on the findings of this study, the non-linear regression model POLS is the best in the curve fitting and economical production of milk. The results show that with milk yield corrected using the optimal feed intake by the POLS model the ranking of the animals has changed and the breeding value of the animals is more accurately estimated. By using the breeding values estimated in this method, one can select the best animals as the parents of future generations.

The ability to estimate the production function based on the POLS method, which is used to create a standard curve of dairy cows is very high. Our results contribute significantly to the field of animal breeding by shedding light on the role of production functions in enhancing breeding programs and facilitating the identification of high-performing animals. The insights gained from our study could drive improvements in animal selection processes and ultimately enhance milk production efficiency.

Artificial neural networks (ANN) have been widely used for both prediction and classification tasks in many fields of knowledge; however, few studies are available on animal science. The objective of this study was to prediction of breeding values of weaning weight in Markhoz goats based on the Mixed Model Equation (MME) and Artificial Neural Networks (ANNs). Quality control and calculation of descriptive statistics was performed using the GLM procedure of the SAS statistical package. The pedigree file included 5541 kids produced by 261 bucks and 1616 does. In the first step, genetic evaluations and Best Linear Unbiased Prediction (BLUP) of breeding values for weaning weight was computed with the mixed model equations, animal model by DMU program. Later, unique dataset was introduced to the ANN models by the R statistical program. A variety of models including, multilayer perceptron (MLP), radial basis function (RBF) and Support Vector Regression (SVR) were used to build the neural models. The artificial neural networks were trained and several networks (different hidden layers and nodes/ neurons) were evaluated. In artificial neural networks, the data were randomly divided to two parts (75% training and 25% for test/validation). Best architecture was selected according to the mean square of error and correlation. Correlation between true breeding value (BVs predicted by MME) and estimated breeding value (BVs predicted by ANNs) for MLP, RBF and SVR models were 0.72, 0.49 and 0.73, respectively. Analysis of farm data showed that the MLP and SVR models have higher performance than RBF for prediction of breeding values or ranking of individuals.

Complete information of relationship matrix is ​​an important principle to achieve reliable results in genetic evaluations. Two kinds of pedigree errors, including incorrect pedigree information and missing pedigree information, reduce the capability of the livestock model in traditional methods. The purpose of this study was to investigate the different levels of missing pedigree information such as sire, dam and both parents (sire and dam together) on heritability, accuracy of prediction and estimation of breeding value in threshold traits.

Sex-limited traits with heritability of 0.05, 0.1, and 0.2, were simulated. In order to create threshold phenotypes of litter size, 20% of the top-phenotypes were considered as two and the rest were considered as one. Different ratios of missing sire information (zero, 5, 10, 15 and 20), missing dam information (zeros, 5 and 10) and a combination of both missing information (sire and dam together) were created. A threshold model was considered. Heritability, breeding values ​​and accuracy of prediction were calculated by Bayesian statistical method.

With the introduction of different levels of missing pedigree information to the complete pedigree, the amount of heritability decreased. The lowest values ​​of heritability were 0.018, 0.084 and 0.16 for heritability of 0.05, 0.1 and 0.2, respectively. In the complete pedigree, the lowest and highest accuracy of prediction were 0.375 (h2 =0.05) and 0.521 (h2 =0.2), respectively. The lowest values of accuracy of prediction for heritability of 0.05, 0.1, and 0.2 were 0.309 (30% in paternal pedigree defects and 10% in maternal pedigree defects), 0.353 (paternal pedigree defects 20% and maternal pedigree defects 10%), and 0.459 (5% defect in paternal pedigree and 10% defect in maternal pedigree), respectively. Furthermore, missing pedigree information led to a decrease in the average breeding value.

The results showed that, compared to the paternal or maternal missing pedigree information separately, paternal and maternal missing pedigree information together, further reduces the estimation of genetic evaluations.

The aim of this study was to investigate the effect of genotype by environment interaction through calculating the rank correlation between estimated breeding values of bulls in Iran and in Interbull. The data of 2,000,378 records of 305 days of milk, fat and protein production in the first, second and third lactations in years of 2004 to 2021 were used. The records were from daughters of 9603 sires in 3,444 herds. In model one, the breeding values of animals in the multivariate analysis for milk, fat and protein production of first lactation were estimated. In model two, a repeatability model was used to estimate breeding values in the multivariate analysis for milk, fat and protein production from first to third parity. Rank correlation were estimated between Iran and Interbull for bulls which had at least 20 daughters in 10 herds. In the single-parity model, the rank correlations between the estimated breeding values in Iran and Interbull for milk, fat and protein were 0.76, 0.75 and 0.76, respectively. In the multiple-parity model, the rank correlations between the estimated breeding values in Iran and Interbull for milk, fat and protein production were 0.48, 0.43 and 0.51, respectively, which shows the interaction of genotype by environment. The results showed different rank correlations for two types of models used. Therefore, due to genotype by environment interaction, the rank of bulls in Iran could be different from Interbull estimates.

The aim of the present study was evaluation of genetic- economic merit for imported Holstein bull semen daughters in different climates of Iran. These progenies were categorized based on different selection indices of their sire semen exporter countries. In this study, data were milk, fat and protein yield, calving age and calving interval in the first lactation of 270566 Holstein cattle that were collected during 1993-2017 at Animal Breeding Center of Iran. The daughters breeding values were predicted under multi-trait animal model by DMU software. The genetic-economic merit of progeny was estimated using the Lifetime Net Income (LNI) index. Estimated least square means of the LNI index for daughters of imported semen under different selection indices were statistically significant in the various climates (p < 0.01). The highest estimated least square mean of LNI index in Iran was related to daughters whose sire semen came from France bulls (2001- 2011). The results obtained in this study showed that the highest estimated least square means of LNI index in cold, semi-cold, moderate and warm climates were related to daughters whose sires were imported from Germany (2008- 2013), France (2001- 2011), France (2012- 2013) and the Netherlands (2012- 2013), respectively. Thus, it could be concluded that the genetic-economic merit of daughters from imported semen in each climate depends on the selection index of bulls in the semen exporter country.

The objective of current study was to investigate the role of disease prevalence rate of training set and genomic architecture on performance of random forest (RF) and threshold Bayes A (BTA) in threshold traits. For this purpose, genomic population were simulated to reflect variations in heritability (0.05 and 0.25), number of QTL (150 and 600) and linkage disequilibrium (low and high) for 30 chromosomes. To create binary phenotype with different disease prevalence rate, at first, 5 percent of training set animals which had the lowest phenotype average defined code 1 (or diseased) and 95 percent of others defined code 0 (or healthy). This process continued with a 5% increase rate until 50 percent of animals had code 1 in training set. In both random forest and Bayes A methods, genomic accuracy with increase in disease prevalence rate 5 to 20 percent was increased, and afterwards to achieve of 50 percent was decreased. The negative effect of high levels of disease prevalence rate on genomic accuracy was higher than low levels of it. Overall, RF was fluctuation to variations of genetic architecture and disease prevalence rate. Despite the higher accuracy of TBA at different scenarios, RF showed a better performance when high-heritability traits were controlled by a large number of QTLs. Despite the important role of genetic basis of the population analyzed, the best method to predict genomic breeding value of threshold traits depend on disease prevalence rate.

The objective of this research was to investigate the impact of temperature-humidity Index (THI) on milk yield (MY) and fat percentage (FP) trend of Holstein dairy cattle in Mediterranean climate of Iran. Data included 348,868 and 302,851 test-day records, respectively for milk yield and fat percentage from 42,781 and 38,829 cows, respectively. Data were collected from 9 provinces located in the Mediterranean region of Iran between 2001 and 2016. The climate information from the nearest weather stations to each herd was used to calculate THI. Then the average of daily THI for 3 days before test date was considered as an environmental descriptor for milk production traits. Significance test using GLM procedure was applied to consider fixed effects in the model. Random regression model using Bayesian methodology was applied to estimate breeding values (EBV) across different combinations of days in milk (DIM) and THI via and subsequently, genetic trends for production traits were estimated along year of birth and THI values. The results showed that in addition to re-ranking of sires in different DIM, the different ranks were observed across different THI. In the other word, animals had different responses along THI and sires with higher EBVs for lower THI had lower EBVs for higher THI. Across THI, the re-ranking of sires for FP was more considerable than for MY. In other words, animal for FP were more sensitive in response to climate changes. For both traits, negative genetic trends were observed with increasing THI. In addition, in contrast with MY, a negative trend for FP was obtained based on year of birth. It could be concluded that THI has substantial effect on milk production traits and including this factor with applying random regression model could be useful to investigate heat tolerance of dairy cattle herds in Mediterranean climate.

The purpose of this study was investigated the genetic gain, increase of average inbreeding and accuracy of prediction using simulated data under different mating designs. Two level of heritability (0.1 and 0.5) and five maing designs including random mating (rnd), mating based on minimum inbreeding (minf), mating based on maximum inbreeding (maxf),positive assortative mating design based on phenotype (phen) and positive assortative mating design based on estimated breeding value (ebv) were considered. The genetic gain after ten generation in rnd, minf, maxf, phen and ebv mating designs for heritability 0.1 were 0.836, 0.747, 0.952, 0.877 and 1.023 units, respectively, and for heritability 0.3 were 2.979, 2.997, 3.016, 3.303 and 3.595 units, respectively. After ten generation increase of average inbreeding for heritability 0.1 was 0.084 in rnd, 0.038 in minf, 0.353 in maxf, 0.079 in phen and 0.215 in ebv, and for heritability 0.3 was 0.057 in rnd, 0.026 in minf, 0.356 in maxf, 0.092 in phen and 0.177 in ebv, respectively. The results shoewd that the genetic gain in minf design was greater than others mating designs per 1% increase of inbreeding, and minf design was better than other mating designs.

For genetic progress evaluation of growth traits, the pedigree information of 12140 Raeini Cashmere goats in Raeini Chashmere goats breeding station from 1990 to 2010 were used. Investigated traits were included birth weight, weaning weight, 6 month weight, 9 month weight and 1 year weight. Variance components and genetic parameters of each trait was estimated by restricted maximum likelihood and six animal models using Wombat software. Direct heritability of birth weight, weaning weight, 6 month weight, 9 month weight and 1 year weight were 0.24, 0.29, 0.33, 0.35 and 0.37 respectively. Breeding values of animals for each trait was estimated using univariate animal model. Phenotypic, genetic and environmental trends of traits were estimated via regression of phenotypic value, breeding value and difference of breeding value from phenotypic value on the year of birth. Genetic trend of birth weight, weaning weight, 6 month weight, 9 month weight and 1 year weight were estimated 16.71, 85.83, 105.16, 123.67 and 122.53 gr/year respectively (p<0.01). The genetic gain of traits including birth weight, weaning weight, 6 month weight, 9 month weight and 1year weight were estimated 0.483, 0.561, 1.767, 1.647 and 1.465 Kg respectively. Regarding to positive genetic trends estimated for growth traits for Raeini goats, the livestock selection program, based on the breeding value in this breed, seems to lead to a good genetic improvement and this strategy should continue.

The methane production from ruminant production system was estimated to reach 250-500 L per animal per day which has been reported to contribute up to 8-10 % of global warming during the next 50-100 years. The aim of this study was to investigate the correlation among methane emission (predicted by volatile fatty acids) with milk production traits, its components and breeding values (BV) of these traits in Iranian Holstein cattle. The rumen digesta was obtained from 150 cattle through stomach tubing and this population divided into 2 groups with 75 cattle in each (the groups have different milk production BV). Data were analyzed by R.3.3.0. The results showed that methane emission per unit of milk and fat were different in the two groups (P<0.0001). Also, the BVs of milk production, fat and protein traits and daily production of milk, fat and protein had weak to moderate negative correlation with methane emission per unit(P<0.05). The highest correlation was observed between daily production of fat with methane emission per unit of fat (-0.79) as well as daily milk production with methane emission per unit of milk (-0.62). These results showed that methane emission may be reduced by indirect selection per generation for the traits had a high correlation with the gas (daily production of milk and fat).

In order to determine the optimum number of test-day records for the progeny test program of Holstein bulls, 732,140 milk yield test-day were used. These milk yield test-days, which were related to 73,214 first parity dairy cows belonging to 62 herds, had been collected by the Animal Breeding Center of Iran from 1992 to 2016. The correlation of predicted breeding value (EBV) of bulls from ten test-day of their daughters compared with EBV predicted from different number of recorded test-days. The Correlation of predicted EBV from ten test-days with EBV from even, odd, (second, fifth, seventh), (second, fifth, tenth) and (second, sixth) test-day records were estimated to be 0.99, 0.98, 0.98, 0.97 and 0.94 respectively. The results showed that to reduce cost of recording, number of records and generation interval in EBV prediction of bulls with random regression model it is possible to use only second, fifth and seventh test-days instead of ten test-days.

In this research, investigation of genetic, phenotypic and environmental trends of biometric traits in Makuie sheep was performed using collected data from 1989 to 2012 at Makouei sheep breeding station. Estimation of trends was performed by multivariate animal model using the DFREML software. Genetic, phenotypic and environmental trends were estimated by regression of breeding values average, phenotypic values average and average of difference between breeding values with phenotypic values on birth year, respectively. Results showed that heritability estimates for biometric traits including height at withers, height at rump, body length, heart girth and thigh circumference were 0.23±0.05, 0.25±0.05, 0.19±0.04, 0.28±0.05 and 0.06±0.04, respectively; and the highest genetic and phenotypic correlations were between height at withers and height at rump traits (0.98 and 0.93, respectively). Genetic trends of mentioned traits were 0.25±0.07, 0.26±0.07, 0.59±0.05, 0.22±0.09 and 0.23±0.03 cm; phenotypic trends were 0.08±0.08, 0.07±0.05, 0.39±0.06, -0.23±0.16 and 0.08±0.05 cm and environmental trends were -0.16±0.06, -0.15±0.06, -0.20±0.06, -0.45±0.15 and -0.16±0.04 cm, respectively. Total genetic improvement after 24 years was low and the highest improvement was observed in body length and chest girth traits therefore, it can be concluded that despite the positive genetic trend, selection in this herd were not based on breeding value and regular plan.

In some studies, it has been reported that high milk production has a negative effect on animal health and longevity and affects the herd's profitability. Decreased cow health can affect herd profitability through increased rates of involuntary culling and decreased or lost milk sales. Common diseases in dairy cows include mastitis, abomasum displacement, lameness and reproductive disorders such as ovarian cysts and metritis. The objectives of this study were to provide a report on the reasons for culling in dairy herds, estimate the genetic parameters of production traits, diseases and to calculate the spearmen correlation between the breeding value of bulls for the traits and their breeding value for resistance for risk of culling.
Material and In this study, 31550 calving records from 17476 Holstein cows were used. In this study production data and culling reasons for diseases of 47 herds in East Azerbaijan from 1995 to 2012 has been analyzed. For genetic analysis ASReml software has been used. The investigated culling reasons for diseases were including of: mastitis, displaced abomasum, lameness, ovarian cysts and metritis. Estimation of disorders heritability has been obtained using univariate animal model. It has been presented a description statistics of culling reasons for diseases, estimation of genetic parameters of diseases and production traits in the studied dairy herds. Furthermore, correlation between bulls breeding value for culling reason for diseases traits and their breeding value for production traits has been calculated.
The average culling rates of dairy herds in this study was 7.05% (with a range from 2.04% to 26.98%). Results of this study showed that 55.24 % of animals in the herd were culling because of diseases and 44.76% for other reasons such as other diseases, physical injuries, non-economic and financial needs. In the other studies the average culling rate of Holstein dairy cows in northeastern Iran was reported as13.1% (4.4 to 25.3% per herd). Lameness and metritis's culling reasons had the highest heritability among studied traits with values of 0.28 ± 0.010 and 0.21 ± 0.045, respectively. A positive correlation (0.37) between mastitis and milk production was reported (Urib et al. 1995) suggesting that high milk production cows have a high genetic capacity for mastitis occurrence. The highest and lowest estimated repeatability in our study were for the traits of 305-day milk yield (0.47 ± 0.009) and fat percentage (0.25 ± 0.009), respectively. The spearman correlation showed positive correlation between breeding value for milk production and ones for displaced abomasum, lameness and metritis that were 0.197, 0.121 and 0.116, respectively. In the present study, there was a positive and significant correlation between the breeding value of bulls for culling due to displaced abomasum and ones due to lameness and ovarian cysts. So it seems the selection of bulls to resist against the risk of culling due to displaced abomasum helps them to resist against lameness and ovarian cysts diseases. Logistic regression results showed that all studied factors affecting culling due to mastitis. Logistic regression of culling risk factors due to displaced abomasum showed that age and season of parturition are important. Fleischer et al. (2001) reported a significant correlation between 305-day milk production and displaced abomasum disease. Logistic regression results showed that culling due to lameness is related to parity and 305- milk production. The selection of cows based on high milk production, milk fat percentage or body type characteristics could have adverse effects on the occurrence of diseases and resistance to health difficulties. So ignoring these negative correlations among traits in the selection process in the long term affects the dairy herd's profitability. The results of this study showed that about half of the culling percentages in the studied herds was unpredictable and due to diseases. Therefore, with considering of the resistance to diseases in selection strategies, it can be achieving to more survivability of the profitable herds.
The results indicate that most of the studied traits have positive genetic correlation with 305-day milk yield, so probably the cows with high potential for milk production are less resistant to culling for diseases. It is suggested that mastitis and metritis be taken into account in the breeding programs of Holstein dairy cows in the country.

In order to evaluate the impact of the prediction Incomplete pedigree breeding value and genetic trends of Karakul sheep pelt, from real data collected at the Sarakhs of karakul sheep breeding station during the years 1995 to 2015 were used. Studied trait was: degree of Pelt that birth weight as auxiliary variables in the statistical model was used. For every trait, data from 21 years of selection and mating was saved in separate files. And the file of pedigree, sire’s number by 25% (from zero to one hundred percent) was removed randomly and data files for each trait in a state of complete and incomplete pedigree (removed 25, 50, 75 and 100% Number of Sires) under the animal model was used. Genetic trends of the pelt through regression of average breeding value on Birth year estimate that has to remove different percentages Numbers sire (0, 25, 50, 75 and 100 percent) from pedigree to were as follows: 0.0420 ± 0.0061, 0.0161 ± 0.0056, -0.0472 ± 0.031, 0.0161± 0.0056 and 0.0161 ± 0.0056. And heritability were, respectively: 0.74 ± 1.76, 0.72 ± 1.65, 0.71 ± 1.49, 0.73 ± 1.38 and 0.76 ± 1.25. The overall defect in pedigree by increasing percentage of elimination the number of sire, direct heritability reduced to 75% removal sire's number, then place the increase of heritability was the pelt as well as genetic trend by increasing the removal rate Sire Number fell.

In most animal models applied for genetic evaluation of animals, it is assumed that residual and genetic variances are homogeneous in different environments and genotypes, but in situations where data are obtained from different environments or at different times, this assumption may be unrealistic and inaccurate. However, numerous studies have reported heterogeneous genetic, residual, and phenotypic variances for production traits based on different factors such as geographical region, production level and herd. Since the selection decisions in animal breeding rely on estimated breeding values, ignoring the fact that variances are heterogeneous has consequences, including inappropriate selection, loss of expected genetic progress and decreased efficiency of breeding programs. The aim of this study was to investigate the homogeneity or heterogeneity of variances for milk and fat yield in different climatic regions of Iran as well as the impact of various methods of data transformation on reducing or possible elimination of variance heterogeneity and its effects on changing the rank of the top animals in the population of Holstein cows in Iran.
In this study milk and fat yield records which were collected from 1983 to 2014 by the Animal Breeding Center and Promotion of Animal Products of Iran were used. Records of milk and fat yield were adjusted for 305-d lactation and two times milking. Data were grouped based on the Domarten method and Bartlett’s test was used for studying the heterogeneity of variances. The transformation methods of Logarithmic, Square root and Box-Cox were applied for removing heterogeneity. Estimated breeding values (EBVs) for animals were estimated with adjusted and unadjusted data and the Spearman's rank correlations between EBVs were computed to assess the re-ranking of best sires and cows under different percentages of selection.
Results of Bartlett’s test were significant for milk and fat yields in the first three lactations which indicated the heterogeneity of variances. Applying Box-Cox data transformation resulted in homogenous variances milk and fat yields in the first and third lactations, respectively, and square root and logarithmic methods did not remove completely the heterogeneity, but they reduced partly non uniformity. Heritability of milk yield and fat yield after data transformation varied from 0.114 to 0.223 and 0.161 to 0.193, respectively, which were slightly higher than that raw data. Re-ranking of common animals in the top 1% of dams was more than 5% of top sires. Box-Cox transformation method resulted in homogenous variances and excluded top 5% and 2% of sires and dams for milk yield and fat yield from the routine list, respectively.
Based on the current results, it can be concluded that variance components for milk and fat yields according to different geographic areas in Holstein cow population is heterogeneous and heterogeneity of variance affects on genetic evaluation and re-ranking of top sires and dams. Box-Cox transformation method is more appropriate than other methods because of removing heterogeneity of variances as well as having the highest rank correlation and minimal changes in the rank of animals.

The aim of present study was to estimate genetic and phenotypic trends for body weight traits at some ages in Lori-Bakhtiari lambs. In this study a total of 7721 records on birth weight, 6462 records of one month weight, 7275 records of weaning weight 5745 records of 6 month weight, 3326 records of 9 month weight and 3088 records of 12 month weight of Lori-Bakhtiari sheep was used to reveal the genetic and phenotypic trend of growth traits. The data was collected in Shahrekord sheep breeding station during 1989-2010. The SAS statistical software was used to evaluate the effect of environmental factors. Wombat software was employed to estimate genetic parameters and animals breeding value. Environmental factors, like birth year, sex, birth type and the age of mother were significant factors as fixed effects (P