فهرست مطالب nasser emam jomeh kashan

The longevity in dairy cow is the duration of presence of an animal the herd from the date of first calving to the date culled. In present research, the aim is 1) to estimate the genetic parameters of the longevity trait of uncensored animals using random regression model and 2) to study longevity of daughters of sires in the population using the EBV of bulls based on 20 daughters and 3) to test the random regression model (RRM) for EBV prediction of Animals with censored data. Genetic parameters of longevity and breeding value of bulls were estimated by ECHIDNA software using RRM with restricted maximum likelihood (REML) method. The file used for the genetic evaluation contained data on 18,120 daughters from 906 bulls. A number of 20 daughters that randomly selected from each sire to use for EBV prediction. In addition to the daughters used for genetic evaluation (18,120 cows), there were 285,064 daughters of the bulls in the main file. The bulls were grouped according to the Breeding value from high to low based on the area under the normal curve. Then, the mean of longevity of the daughters in each group who were not used the EBV prediction calculated. In a file 10 daughters were randomly selected of 20 daughters of each bull and their months of longevity divided by 2 and 3. These data were considered as censored in the data file. The permanent environmental effect and the additive genetic effect had the largest and smallest contribution to the total variance, respectively. The heritability of the trait was low (0.003-0.032) which is in agreement with results of other report. The difference of mean longevity between groups (LSD test) was significant (P˂0.01). Also, the increase of the mean of EBV of the groups, the mean of longevity of their daughters in the population increased. The correlation of EBV of animals with censored and uncensored data state were +0.815. In the split the low heritability of the longevity trait, using the sperm of superior bulls in terms of EBV of longevity can improve the longevity in the population. Also, it is possible to evaluate and select the bulls with a low progeny number and increase the mean of longevity in the population of Holstein dairy cows. Moreover, the EBV of longevity for young and producing animals can be predicted by RRM.

Milk production and fertility are important traits for genetic evaluation of bulls in breeding programs. The goal of this research was to investigate the reproduction and fertility status in Iranian Holstein dairy cattle and effect of days open on the milk production and genetic ranking of bulls. A total number of 706,653 test day records of first parity of 78,517 Holstein cows in 448 herds during the years from 1991 to 2016 were used. The phenotypic and genetic relationship of the amount of milk production in 270 days and the number of open days and their genetic parameters were estimated through a two-trait model. Also data were analyzed using a random regression model and predicted the breeding values of bulls. The results showed that in the studied population, 7.7 and 60% of the cows in the herds became pregnant by 45 and 112 days after calving, respectively and 54.6% of successful pregnancies occur after 90 days in milk. The heritability of 270 days milk and open days and their genetic correlation were estimated of to be 0.257 (±0.016) and 0.0314 (±0.004) and 0.538 (±0.06) respectively. The range of estimated heritability of 270 days milk for first to ninth groups were 0.11-0.26. Days open was a source of variation of the parameter. The results showed that number of open days affected the predicted breeding value of bulls and their ranking. It is concluded that, in sire evaluation programs in order to increase the accuracy of breeding value prediction of the sires it is necessary to include the variable of number of open days in the statistical models.

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.

Breeding between Indus cows and Taurus cows in the Sistan region of Iran has been done in recent years through the use of liquid sperm, frozen sperm, or foreign bulls. There are few studies in Iran on the effects of interbreeding programs on Sistani cattle. The present study was performed to identify mononucleotide polymorphisms (SNPs) based on RNA-Seq data in purebred Sistani cows and its crosses with three foreign breeds of Holstein, Simmental, and MonteBilliarde cows.

In this study, RNA-Seq data were used for pure Sistani, Sistani and Holstein, Sistani and Montebeliarde, Sistani and Simmental cattle. For this purpose, first of the tail vein of purebred cattle mixed with Holstein, Simmental, and MonteBilliarde. (4 treatments and for each treatment two biological replications) in the same environmental, nutritional, and managerial conditions located in the breeding center of Sistani cattle in Zabol. blood was drawn.

SNP discovery analysis was performed on transcriptome using SAMtools software package, which led to the discovery of 152496, 177042, 134285, and 163362 SNPs in pure Sistani breeds and its crosses with three foreign breeds of Holstein, Simmental, and Montebeliarde bulls. In this study, there was no direct relationship between the number of SNPs identified and the length of chromosomes. Also, 12 types of SNPs were identified, of which four types were transition and eight types were transversion. The most common known SNPs were transition which was 71.84% in pure Sistani, 72.65% in Sistani and Holstein, 72.60% in Sistani and Simmental, and 71.94% in Sistani and MonteBilliarde.

Overall, the results of this study confirmed that RNA-Seq technology is an effective, economical, and efficient method for identifying SNP loci. Factors influencing RNA-Seq evaluation are such as the number of samples.

The aim of this study was to evaluate the effect of fosfomycin antibiotic as well as yeast probiotics of S. bulardii and S. cerevisiae on performance, antioxidant activity and expression of interleukin-6 gene in Holstein calves. In order to test, 20 newborn Holstein calves were distributed in 5 treatments and 4 replications (each replication including 1 calf) for 60 days after feeding with colostrum in a completely randomized design from the age of 3 days. Experimental treatment diets include 1: control (without antibiotics and probiotics), 2: fosfomycin (0.165 g/kg BW), 3: milk-soluble S. boulardii (1 g/kg BW), 4: milk-soluble S. cerevisiae (1 g/kg BW) and 5: A mixture of S. boulardii and S. cerevisiae were dissolved in milk (1 g/kg BW each). The amount of feed intake, weight gain and feed conversion ratio of none of the treatments were not significantly different from the control (P>0.05). The amount of serum glutathione peroxidase and superoxide dismutase enzymes as well as the amount of interleukin-6 gene expression in the whole period in calves that used a mixture of S. boulardii and S. cerevisiae had the highest significant difference with the control (P<0.05). In general, the use of a mixture of yeast probiotics had a greater effect on the antioxidant system and immune system of calves than their separate use and also compared to antibiotics in calves nutrition.

The objective of this study was predicting breeding values for sires and cows at an early stage of cows’ first lactation, to enable early selection of sires. Accuracy of predicted breeding values were investigated using cross validation method. Data consisted of 2,166,925 test-day records from 456,712 cows calving between 1990 and 2015. (Co)-variance components and breeding values were estimated using a random regression test-day model and the average information Restricted Maximum Likelihood method. Legendre polynomial functions of order 3 were chosen to fit the additive genetic and permanent environmental effects and homogeneous residual variance was assumed throughout lactation. The lowest heritability of daily milk yield was estimated to be 0.14 in early lactation, and the highest heritability of daily milk yield was estimated to be 0.18 in mid lactation. Cross validation showed high positive correlation of predicted breeding values between consecutive yearly evaluations for both cows and sires. Correlation between predicted breeding values in early lactation (5-90 days) and late lactation (181-305 days) were 0.77-0.87 for cows and 0.81-0.94 for sires. These results show that we can select sires according to their daughters’ early lactation before they finish first lactation. This can be used to decline generation interval and increase genetic gain in Iranian Holstein population.

Machine learning methods have been used in genetic studies to build models capable of predicting missing genotypes for both human and animal genetic variations. Genotype imputation is an important process of predicting unknown genotypes. The objective of this study was to investigate the idea of using machine learning as imputation to compare the family-based methods and tried to offer improving the imputation performance in different scenarios. Also, the accuracies of different methods i.e. Support vector Machine; SVM, Random forest; RF are compared. The final population were simulated in the form of different family structures. Therefore, 100 families including one sire with different number of genotyped progenies (2, 3, 4, 5 or 7) were simulated. The number of markers was set to 5000 for whole genome. The sires in families and other scenarios such as, BothParents, sire/dam and one progeny, sire and maternal grandsire were defined to investigate the ability of learning machine algorithm for imputation. The imputation accuracy ranged from 0.78 to 0.99 in different scenarios. Also, least amount of imputation accuracy were achieved for sire and maternal grand sire scenario with both methods. Increasing in number of progenies from 2 to 3 was considerably increased in imputation accuracy (SVM and RF). The imputation of non-genotyped individuals based on parent-offspring trios and close relatives paired is possible. But, the use of child- one parent genotyped, BothParents genotyped and sire and maternal grandsire genotyped, average imputation accuracy would not exceed 85%. While genotyped progenies are the best source of predicted genotypes for ungenotyped individuals and if the number of progeny is more than 4, the imputation accuracy is increased more than 95%. These results confirmed, that the performance of machine learning methods in family of trios has a good accuracy and computational speed, which can be used in estimated breeding value.

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.

The present investigation was undertaken to study reason of culling, non genetic effects and genetic parameters of longevity with two method linear model and Weibull. A data set of information from Holstein dairy cows of Isfahan province (Iran) was used for this study. The culling date of 36340 animals from 65 herds recorded in 1993-2014 was used. From 33219 culled cows in these years were 27.9 and 72.1 percent for voluntary and involuntary culling respectively. Lifespan and Length of productive life (LPL) were analyzed for longevity. Functional productive life (FPL) was estimated by adjusting LPL for first lactation milk yield relative to herd average. The mean of lifespan and length of productive life were estimated to be 954 and 1710 (days) respectively. The results with GLM procedure showed that HYS and age at the first calving had significant on length of productive life. The results with Lifereg procedure with Weibull function showed that effects of herd, year of first calving (p