مقالات رزومه دکتر صادق علیجانی

The genome-wide association study (GWAS) is a powerful approach to identify genomic regions associated with fertility traits that explain a significant portion of the genetic variance associated with these traits and identify the relevant causal mutations. Evaluating the correlation between each genotyped marker and trait is an essential strategy for GWAS studies that examine the effects of all markers by considering their possible interactions, environmental factors, and even mutual effects between markers. Recently, machine learning methods have been introduced to genomic topics, and the basis of these methods is different from the common methods of genomic evaluation. The machine learning method is used to estimate the genomic breeding values of the candidate animals by considering the training data (genotypic and phenotypic information of the reference population). One of the key advantages of this method is the ability to analyze large data. Machine learning is a branch of artificial intelligence whose goal is to achieve machines that can extract knowledge (learning) from the environment. A variety of machine learning methods (random forest, boosting, and deep learning) are used to model genetic variance and environmental factors, study gene networks, GWAS, study epistasis effects, and genomic evaluation. Random forest is one of the machine learning methods that has been successfully used in various fields of science. This research was conducted to identify markers and genes related to reproductive traits such as calving interval (CI), days open (DO), daughter pregnancy rate (DPR), and age at first calving (AFC) in Iranian Holstein dairy cattle. These traits have already been investigated with the ssGBLUP method and using a smaller sample size. However, in the present research, by using more genotyped animals, a random forest algorithm was used to identify markers and genes related to reproductive traits.
The records used in this research were provided by the National Animal Breeding Center and Promotion of Animal Products of Iran and included AFC, DO, CI, and DPR related to the genotyped bulls' daughters. In this research, the pedigree information of 2774183 animals was used. The genotypic information of the markers related to 2419 Holstein bulls was used. Genomic data quality control was performed using factors such as the number of genotyped SNPs per animal (ACR), the number of genotyped animals per SNP (CR), Hardy-Weinberg equilibrium (HWE), and minor allele Frequency (MAF). When filtering genomic data, the markers whose MAF was less than 5% were removed, and then the samples whose genotyped frequency was less than 90% were identified and removed. Then, the markers whose genotyping rate was less than 95% in the samples were identified and removed. Finally, the SNPs that deviated from the HWE test (P<10-6) were excluded from the analysis as a measure of genotyping error. To control the quality of genomic data, PLINK 1.9 software was used. Then Ranfog software was used in the Linux environment to perform analysis through random forest algorithm.
By using the random forest algorithm, a total of 21 important SNPs were observed, then important fertility trait candidate genes were identified by the gene ontology method, and 62 genes were within 250 Kb of these SNPs. The most significant SNP was observed for AFC. The main SNP for AFC is in ARS-BFGL-NGS-22647 BTA3, for CI is in ARS-BFGL-NGS-114194 (BTA11), for DO is in BTA-74076 -no-rs (BTA5), and for DPR is in ARS-BFGL-NGS-32553 (BTA26). The researchers, who studied fertility traits in Nellore cattle using machine learning methods, identified MPZL1 and CD247 genes on chromosome number 3 and this gene was associated with age at first calving. Many pathways of cell biology affect the performance of reproductive traits. Research has reported the relationship between the CD247 gene and pathways of biology, including cell development and function. Research has shown that the IFFO2 gene plays an important role in the molecular structure of cells, as well as in the mechanism of blastocyst formation, embryos, and the length of gestation in cattle. In a study conducted on the mouse population on the structure of the flagellum and the sperm maturation process, the role of the ALDH4A1 gene in the sperm maturation process was reported. The association of the RPS6KC1 gene with pregnancy rate and antral follicle number in Nellore heifers has been reported. The KAT2B gene is a transcriptional activator that plays an essential role in regulating the correction of histone acetylation and plays an important role in improving carcass quality, muscle and fat development, and metabolism in native Chinese cattle. In addition, they play a key role in regulating biological processes and are related to cell growth, metabolism and immune system function.
  According to the objectives of this research, new information on markers and candidate genes related to reproductive traits in Iranian Holstein dairy cattle was reported. The markers and candidate genes identified in the present research can be used in genomic selection to improve the reproductive traits of Holstein dairy cattle.

The ability of an animal to survive in the breeding period is called the survival trait. One of the most important features of survival analysis is unfinished (censored) observations. In other words, the event under study does not necessarily occur at the time of recording the observations. Thus, survival observations and data are generally incomplete, and events occur for some people but for others there is no observation for that event. In general, nonparametric regression methods (Kaplan-Meier estimator, Cox hazard ratio model) and parametric methods are used for this type of analysis more than other methods. Survival and longevity analysis is performed by linear, threshold and hazard ratio functions methods. Linear models are easier to use, but due to the nonlinearity of the survival trait, they are skewed, and the estimates of heritability with this method are low and close to zero. Threshold models are more accurate and obtain higher genetic estimates. In the analysis of hazard ratio method, the accuracy is higher than previous models due using the more information (use of censored data), but it is time consuming and its computational load is higher. Therefore, the aim of the present study was to estimate the genetic parameters and the effect of fixed factors on the survival of Makuie sheep lambs up to one and two years of age using the proportional hazard function under Weibull distribution.

In this study, 2332 records of Makuie lambs that collected between 1986 to 2010 in Makuie sheep breeding station, were used. In the first step, fixed factors affecting longevity (gender, type of birth, year of birth, month of birth, birth weight, weaning weight, weaning age and age of dam) Was investigated by LIFEREG procedure of SAS statistical software version 9.4 under Weibull distribution. Then significant effects were used in the survival analysis with the proportional hazard model. Survival up to one and two years of age were analyzed using the Weibull hazard model by SURVIVAL KIT software, version 6.12. SURVIVAL KIT software is able to provide components of genetic variance and can estimates breeding values for animals using mixed models. Survival from post-weaning to one year (for analysis up to one year of age) and two years (for analysis up to two years of age) were assessed by day.

In the analysis of survival from weaning to the one year of age, 38.12% of observations and in the study of survival up to the two years of age, 13.68% of the observations were censored. According to the study, the year of birth had a significant effect on the survival of lambs. The year of birth affects the amount of rainfall and ultimately the availability of food resources by affecting the pasture, and thus changes the quality and amount of nutrition. survival up to one year of age Lambs born in 2004 and for survival up to two years of age lambs born in 1994 had the lowest risk of elimination. Lambs born in 2010 had the highest risk of elimination. Scrutiny the effect of birth month on the survival of lambs, it was found that for survival up to one year of age, lambs born in April had a higher risk of elimination. However, for survival up to two years of age, lambs born in May had the highest risk of elimination and the amount of risk for May compared to March was 1.96. Lambs born in May reach autumn at a young age and during a period of rapid growth. Because the studied flock is kept semi-densely, access to feed for these lambs is reduced and they are removed from the flock due to their low weight and insufficient growth rate. Birth weight is one of the most important components affecting the survival rate and directly affects the elimination rate. Threshold weights have the highest elimination risk ratio and the weight of 3.5-4.7 kg has the lowest elimination risk ratio for survival up to one and two years of age. There is a curved relationship between elimination risk and birth weight, so with increasing birth weight to medium weight (3.5-4.5 kg), the risk ratio decreases and then increases. Mortality at low weights is due to the inability to control body temperature and high mortality at high birth weights is due to infertility. Weaning weight is an important factor influencing survival after weaning. Similar to birth weight, weaning weight has a curved relationship with survival. So that with increasing the weaning weight from 10 kg to 19 kg, the risk of elimination for survival to one and two years of age decreases from 1.8 to 0.97 and 0.93, respectively, and then with increasing the weaning weight, risk ratio reaches to 2.53 and 2.38, respectively. Lack of growth-induced production encourages the elimination of low-weight weaned lambs. So the elimination risk ratio is high at low weights. But as the weaning weight increases, the desire to keep it for later periods will be greater, and thus the removal pressure will be lower. The average weight of weaning in the herd is equal to 19.01 kg, the results also show that the lowest elimination risk ratio and consequently the highest survival for weaning weight is 18-20 kg. Therefore, in order to increase the survival and reduce the elimination risk ratio, management and corrective programs can be designed to select the optimal weaning weight. For survival up to one and two years of age, the risk of elimination for male lambs was higher than females. In the period between weaning until the age of one year, the elimination risk ratio for male lambs was 2.17 relation to females and for survival to the age of two years, the elimination risk ratio of males was 2.61 Compared to females. This difference in elimination rate can be attributed to sex-related factors, sex characteristics that may not have yet been discovered, systemic diseases, different management practices for males and females, and high elimination rate for males due to surplus fattening. There are three types of births in Makuie sheep (singles, twins and triplets). Lambs born with twins and triplets have lower birth weight and weaning weight than single lambs. Due to the above topics, low birth weight and low weaning weight, reduces the survival and, increase the risk of elimination. Another reason for the reduced survival of multiple births is the limitation in dam milk production, which can be due to poor maternal genetics or inadequate nutritional intake. Heritability of survival to one and two years of age for Makuie sheep lambs according to the censorship ratio of 38.12% and 13.68% (respectively) was 0.062 and 0.079, respectively.

Since heritability was estimated at a low level, control of environmental factors and management will play an important role in improving the survival of Makuie lambs.

In the livestock industry, the accurate genetic evaluation of economic traits pre-request the existence of homogeneity in residual variance, however; in practice, such different production levels and data recording at different times and environments are unlikely to result in the homogeneity of residual variance. The aim of this study was to investigate the genetic variance of the residual variance for Egg Weight and Age at Sexual Maturity of Azerbaijan native chickens. Data set consisted 8283 Egg Weight (EW) records and 6608 Age at Sexual Maturity (ASM) records related to 9 generations from the indigenous chicken breeding centers in the west Azerbaijan. To investigate the genetic variation of the residual variance was using the BLUPF90 software. Heritability of EW and ASM in a single trait model was estimated to be 0.288 (s.e. =0.022) and 0.355 (s.e =0.127), respectively. The heritability of residual variance of these traits was estimated 0.00484 (s.e. =0.004) and 0.00005 (s.e. =0.002), respectively, indicating the slight possibility of their genetic progression by direct genetic selection. The values of genetic correlations between traits and their residual variance were estimated to be -0.154 and -0.076, respectively, suggesting that genetic selection for EW enhancement would improve the genetic value for their uniformity, but about ASM, the expected results will not be very favorable if the trait is reduced. In conclusion, for accurate estimation of breeding value and ranking based on production traits in poultry, special attention to variance heterogeneity and necessary corrections is inevitable.

Varroa infestation is undoubtedly the greatest threat and challenge facing Apiculture today. This external parasite inevitably lives in the bee colony and causes irreparable damage to its colony and the subsequent honey production. One of the proposed strategies in this regard is the use of pesticides, which have a negative impact on the health of bees and honey consumers. To avoid these negative consequences, safer alternative methods of controlling mites are needed, including the use of resistant genetic strains and breeding selection programs to establish colonies with relative resistance to mites. In honeybee colonies, there are several physiological and behavioral mechanisms for Varroa resistance that by examining their relationship with identified resistance genes, the genetic basis of Varroa mite resistance in honeybees is identified and can used in breeding programs. The aim of the present study was to identify single nucleotide polymorphisms (SNPs), in a sample of the Iranian honeybee population, in the candidate gene (the dopamine receptor gene (DOP3)) effective on defense behaviors of honeybees against the varroa mite.

For this purpose, a total of 10 drone bees (5 susceptible and 5 resistant) were selected and their DNA was then isolated using a CTAB-based method. The PCR was carried out based on specific primers in the UTR region sequence of the DOP3 gene. The quantity and quality were then determined using the nanometer method. After a single band (900 bp) of the expected size, product purification and sequencing (Sanger method) were performed. The display of the outputs and the determination of the quality of the raw sequence (phred index) took place with the FinchTV software and the alignment with BLAST and clustering with the MAFTT software.

In this study, differences were observed in several regions of the nucleotide sequence of the UTR region of DOP3 gene, the most important difference in the nucleotide sequence between sensitive and resistant individuals in the two regions. One is in the nucleotide region of 428 to 437 forward readings as many as 9 bp nucleotides and the other is in the nucleotide region of 715 to 720 forward readings as many as 6 bp nucleotides, which in both, mutations of the deletion type have been performed.

Evaluation of the final results showed significant difference, in type of deletion/addition with the size of 6 and 9 bp between two groups (sensitive and resistant to Varroa), respectively which can be used in the molecular identification of resistant colonies and breeding programs to produce Varroa mite resistant colonies. No such deletions from this gene have been reported so far.

The graphical representation of daily milk yield during lactating period is lactation curve. There are many advantages of evaluation of the lactation curve in dairy cows, such as designing suitable breeding and management strategies for dairy cattle, genetic evaluation of dairy cows, and the prediction of the total milk yield of cow To explain the flow of milk production over the course of lactation in dairy cows, various mathematical models have been developed (Wilmink 1987; Wood 1967).There are many earlier studies that have successfully applied lactation curves in modeling the milk yield-DIM of Iranian Holstein. In all of these research the classic or Frequentist statistical methods have been used for parameter estimation and statistical inferences. The parameters of these models have not been estimated for Iranian dairy cows using Bayesian method. In the Bayesian approach, the parameters of the model are random variables, and inference is made on parameters using their posterior distributions (in some cases without the assumption of normality of the studied data) and need for huge data (Iqbal et al. 2019).This study was Bayesian estimation of the parameters of Wood, Milkbot, Gompertz, Dijkstra, Cobby and Le Du, Von Bertalanffy, Brody and Logistic mathematical models for the lactation curve. This was done using 30618, 30685 and 30627 days in milk (DIM) records of milk yield, fat percentage and protein percentage, respectively. These records were related to the days of 5 to 305 days of the first lactation period of Iranian Holstein cows with 3685 cows out of 350 herds. These data have been collected by National Breeding Center and improvement of animal production of Iran in registered herds of Holstein cows.

Data were initially adjusted for significant fixed effects that were herd-test-date (HTD) and age at calving. In order to be accurate and due to the high computational needs of Bayesian method, in this study, 30618, 30685 and 30627 records of milk production test day, fat percentage and protein percentage related to the first lactation period belonged to 3685 Holstein cows out of 350, respectively. For test-day records of milk yields, an outlier control assessment was conducted. 99.73 % of the observations accounted for data within the μ ± 3 standard deviations range. Records outside this range have been taken into consideration outliers (Junior et al. 2018). Bayesian inference was used to estimate the subsequent distribution of both unknown parameters of each lactation curve model, using a mixed nonlinear model to which the random lactation effect of each cow was added to account for the individual lactation curve of each cow. The parameters of lactation curves were estimated using the cow's test day records in the MCMC procedure of SAS software. To consider the individual lactation curve of each cow, the effect of each cow was used as a random effect in all nonlinear models. For sampling of the posterior distribution of parameters, the Monte Carlo Markov chain sampling algorithm was obtained by considering the burn-in period, sampling interval (thin) and number of cycles of 150,000, 100 iterations and 400,000 cycles for each, respectively. The effective sample size and Geweke detection test was used to evaluate the sampled amount and calculate the convergence indices. Comparisons of the models were made based on the Deviance Information Criterion (DIC). To determine the significance of the difference between the two models, if the difference between the two nonlinear models was less than 10, the DIC index was considered non-significant and if it was 10 or more, it was considered significant (Iqbal et al., 2019).

By examining the statistics of functions and convergence indices, Brody function for milk production and Wood function for fat percentage and protein percentage showed the most suitable model and better fit. The Convergence has been achieved according to Geweke test (Geweke 1992) statistic P-value. In the protein percentage fitting curve, Wood model was selected despite the significance of parameter a in the Geweke test (P <0.05) due to the appropriate trace plot. Effective sample size and the simulation sample size (4000) indicate convergence in all parameters. The differences between DIC values were found greater than 10 points for most of the cases, indicating high significant difference between the fitted models. In the results, the estimated parameters of the Brody function for milk production were 37.219, 0.544 and 0.084 for a, b and c, respectively. For milk fat percentage and milk protein percentage, the estimated parameters of Wood function were 4.29 and 3.53 for a, -0.08 and -0.04 for b, -0.0008 and -0.0004 for c, respectively. For the first 30 days, a modified gamma function gave the best fit for the first lactation (Sherchland et al. 1995).The Wood curve was superior to the Gompertz function in fitting the data and, hence, it was used for biological inference (Hansen et al. 2012).In another study by Bangar and Verma (2017) to compare four nonlinear models, Wood, quadratic model, mixed logarithmic model and Wilmink to fit the shape of lactation curves of milk production and its production traits in Gir crossbred cows in India, Wood model as the best model for Fitting of milk production data and production traits were introduced.

Bayesian method can be used in modeling complex nonlinear functions for lactation curves, especially with a small amount of data. Biological interpretation of these parameters makes it possible to use these estimates in a selection index to genetically modify the lactation curve. This shows that Brody and Wood functions, respectively, as the best model in predicting milk production and production traits in Iranian Holstein cows in the first lactation period.

In this study, body weight records of 19171 American Simmental beef cattle including 8758 bull and 10413 cows from 2000 to 2016 were used to estimate genetic parameters and trend. We conducted genetic evaluation of data from 5199, 10926, 7362 and 5636 genotyped beef cattle of gene-based single nucleotide polymorphisms (SNPs) for birth weight (BW), weaning weight direct (WWD), yearling weight (YW) and post-weaning weight gain (PWG), respectively. Genetic parameters estimated using univariate and bivariate animal models and BLUPf90 software. Heritabilities were estimated for BW, WW, YW and PWG 0.38 ± 0.02, 0.24 ± 0.01, 0.35 ± 0.01 and 0.22 ± 0.02 using univariate analysis, respectively. Genetic correlation between BW and PWG was 0.31. The highest and the lowest genomic accuracy were estimated for WWD (0.68) and PWG (0.27), respectively. Genetic trends were estimated for BW, WW, YW and PWG 0.13, 0.28, 3.43 and 0.05, respectively. In addition, slight changes were observed in the genetic trend of all traits, especially weaning weight direct and yearling weight across years of study which indicates the existence of specific selection goals and criteria in each of these traits. In general, three large-effect closely linked QTLs identified on BTA6 at 37 Mb, on BTA7 at 90 Mb and on BTA14 at 22 Mb. Because correlations were favorable between body weight traits, breeding program should be based on the simultaneous selection of these traits through the selection index.

The aim of the present study was to estimate the molecular characteristics, structure, genetic diversity and genetic bottlenecks in the three sheep populations native to Iraq (Awassi, Naaimi, and Arabi). For this purpose, 12 microsatellite loci with chromosomal distribution distributions (1, 2, 5, 9 and 14) were used according to the recommendations of FAO and the International Society for Animal Genetics (ISAG). Sixty samples of whole animal blood (both sex) were randomly collected from the relevant populations in Karbala, Najaf and Babel provinces. The genomic DNA extraction, quality and quantity, preparation of PCR, electrophoresis photography was done accordingly based on standard available methods. After observation of raw genotype per investigated loci, allele binning was done for minimizing genotyping errors. More than 9 types of molecular statistical indices (allele frequencies, observed and effective number of alleles, observed and expected heterozygosity, PIC, Wright coefficient, Shannon index, F-statistical indices, genetic distance and the bottleneck) Checked out. The results were presented in two parts: evaluation of microsatellite loci utility and evaluation of genetic diversity within and between the population. The results of the first part showed that the highest polymorphism was observed in OarFCB226 and TGLA13 markers and the rest had the lowest polymorphism. The results of the second part showed that the Awaasi and Arabi breed have the highest and lowest diversity, respectively. The highest genetic distance was between Arabi and Naaimi breeds and the smallest was between Naaimi and Awassi breeds. Furthermore, bottleneck L-Shape curve showed that the studied populations are in a genetic predicament.

Freezing is one branch of cryobiology knowledge that process to protecting and long term keeping of cell in very low temperature. Sperm freezing commonly done in infertility treatment centers and keeping sperm banks and livestock industry. Sperm can survive decrease out of body and environment temperature. freezing is so important one of influential destructive factors in reducing duration of sperm storage is creation of ROS. It needs very low, but it is increasing has destructive effects on sperm (lenzi et al. 1994). Use of antioxidants in sperm extender have better results to deal with these reactive oxygen species. It is believed that ROS induces lipid peroxidation in the sperm membrane and The peroxidation of the resulting fatty acids has toxic effects on the sperm that Leading to a decrease in sperm function (Sanuka and Corps 2004). Antioxidants are compounds that control, stop and neutralize intermediates derived from the energy production process in the electron transport chain of cells (aerobic activity) known as active oxygen species, and in a group of free radicals (Zinee et al. 2000). Adding antioxidants to semen diluent during freezing of sperm improves the quality and improvement of semen parameters such as motility, viability and acrosome membrane integrity (Mironoksuk et al. 2018). Selenium is an essential micronutrient element to human health Which is toxic at high concentrations. Selenium is a constituent of selenoproteins that they have an enzymatic and structural role in biochemistry. Selenium is the main component of selenoenzymes in the center of all of these proteins, there is the amino acid of selenocysteine Which acts as an oxidation-reducing agent. However, one of the limiting factors for selenium consumption is bioavailability (the rate of entry into circulation and tissues) and its toxicity (Tarz et al. 2007). Therefore, the use of other forms of selenium that is less toxic and more beneficial is more appropriate. Selenium nanoparticles are low toxicity compounds and bioavailable and effectively increase the production of selenoproteins in the body (Peng et al. 2007). Selenium is a cofactor or activator of the glutathione peroxidase enzyme Which is one of the strongest antioxidants in the body and catalyzes the decomposition of lipid peroxides and hydrogen peroxide (Hill et al. 1996). The glutathione peroxidase enzyme destroys free radicals within the cytoplasm (Ozball et al. 2008). Typically, the concentration of most of the elements in the liver is higher than the other organs, but in the case of selenium, it has the highest concentration in the testis. High concentration of selenium in testis indicates the protective role of this element and its related enzymes in spermatogenesis (Schumberger et al. 1983).

Semen was collected with an artificial vagina twice a week from 4 selected adult Ghezel rams during non-reproductive season. Ejaculates were immediately evaluated primarily for parameters including volume, concentration, motility, viability and abnormal sperm. And if they had the required standards (high concentration of 3 billion sperm per ml, motility above 70% and volume greater than 0.5 ml), they were selected as suitable samples for dilution. The semen was diluted with extender with no antioxidants (control) and containing 1 µg/ml Nano Selenium and 2 µg/ml Nano Selenium and then immediately aspirated to 0.25 ml freezing straw. After recording, treatments were packaged and refrigerated in 5°C for 1.5 hours. Then, the packaged straw containing semen were placed on 4 cm above the liquid nitrogen, then were immersed in liquid nitrogen immediately after 10 minutes. Qualitative parameters of sperm included viability, total motility (TM), progressive motility (PM), plasma membrane integrity (HOST), percentage of abnormal sperm and acrosome membrane damaged was studied in days 0, 15 and 30 of freezing process.

Percentage of viability, total motility, progressive motility, plasma membrane integrity (HOST) of sperm decreased significantly (p < 0.01). Also the percentage of acrosome membrane damage and abnormal sperm increased during experiment (p < 0.01). However, the addition of different levels of Nano Selenium to semen extender significantly increased the percentage of viability, percentage of sperm with total motility, progressive motility and health plasma membrane (positive HOST) on days 0, 15 and 30 compared with control group (p < 0.01). Also, the addition of different levels of Nano Selenium to extender significantly decrease the percentage of abnormal sperm and acrosome membrane damaged (p < 0.01).

According to the results of this study, the addition of different levels of Nano Selenium, especially Nano Selenium 1 µg/ml to ram sperm extender increases the quality of sperm during storage.

One of the disadvantages of estimating genetic parameters based on the absolute weight of sheep at a given age is that some genes affect growth only in a specific period of animal life. There are also pleiotropic effects between weight cross-sectional traits of different ages. Therefore, that selection for one trait causes a correlated response in other traits. So focusing on all the stages of the growth curve and the components that characterize the curve can produce more genetic progress. Research results show that growth curve variables are heritable in different species. Therefore, it is possible to modify the growth curve through selection. Understanding the genetic and environmental relationships between different weights, maturity and growth rates at all stages of development is essential for designing a breeding program to improve production efficiency over the lifetime of the animal. Therefore, identifying and locating QTLs controlling growth curve parameters as heritable traits could be a useful research scenario for investigation. Ultimately, it will increase meat production.

Growth curve parameters were estimated using the Brody model and data of 27537 Ghezel sheep. These population pedigree checked by software pedigree viewer and the two families were selected for this project To do present research two half-sib family, 51 offspring of two genera of sheep breeding in the Ghezel Miandoab Breeding station of were taken. The chromosomal positions were located on 2 and 5 of the microsatellite were used to determine the genotype. The marker spacing on the chromosomal map was under 30CM. The DNA extraction was performed using Sammady Shams and colleagues from the whole blood and 4% metaphor agarose gel was used for multiplication of microsatellite sites using Touchdown PCR and. UVDOC software was used to score bands. Descriptive statistics were calculated for molecular indices of each markers locus using POPGENE software. Upon completion of genotyping three files map, genotype and records phenotype were prepared and then evaluated normality raw data and correction for fixed effects (gender, type of birth,) by the procedure GLM in SAS 9.2 software made in 2015 was carried out. The analysis results microsatellites showed that polymorphism fit there, but alleles heterozygous rams similarity high, suggesting consanguinity with alleles of the sheep show. The method used to The QTL analysis was identified the relationship between genetic markers and QTLs by two markers.

The results of microsatellite analysis showed that there is a good polymorphism in the studied population. Finally, the results of the two families indicated no significant QTL identification on chromosomes 2 and 5 for traits of brody growth curves parameters (A, B, C).

No significant differences were found in the QTL analysis. Of course, the plan was criticized because of the low number of markers, the number of families and each family member. These factors may, in turn, be the reason for the lack of QTL identification in this design.

The egg quality is one of the most important economic factors for layers that are crucial for hatchability and marketing. Given the low number of egg quality traits records, one of the approaches in estimating phenotypic and genetic parameters is multi trait analysis. The aim of this study was to estimate the phenotypic and genetic parameters of west Azerbaijan native hens egg quality traits by multi-trait analysis. For this purpose, 2000 eggs of 14th and 15th generation of pedigree hens were purchased from west Azerbaijan native fowl breeding station and for measuring egg quality traits were transferred to laboratory. The genetic and phenotypic parameters of egg quality traits were estimated by multi-trait analysis using SAS and WOMBAT softwares. The generation-hatching time and storage duration in refrigerator had significant effect on egg quality traits. The mean of some egg quality traits were: egg weight 55.306 g±3.94, egg shape index 75.192±2.9, egg shell percentage 9.389 %±0.73, albumin percentage 60.160 %±2.49, yolk percentage 30.466%±2.35, yolk index 35.928±3.13 and yolk pH 6.442±0.13. The highest and the lowest coefficient of variance were calculated in air cell height and albumin pH, as 16.947 % and 1.366 %, respectively. The highest positive phenotypic correlation was observed between egg weight and egg volume (0.644), and the lowest one was observed between pH of albumen and yolk weight (0.004). The highest positive genetic correlation was observed between egg weight and egg shell surface area (0.999), the highest negative genetic correlation was observed between albumen percentage and yolk to albumen ratio (-0.994). The lowest positive genetic correlation was estimated between yolk index and yolk weight (0.002) and the lowest negative genetic correlation was evaluated between yolk index and dry matter of yolk (-0.005). The range of estimated heritability of studied egg quality traits were 0.212 (albumen percentage) and 0.015 (pH of albumen). Generally, the results indicated that the egg quality traits of Azarbaijan indigenous hens have relatively high phenotypic and genetic correlation, which in order to decreasing the cost of data recording, the number of recorded traits could be decreased. The heritabilities of egg quality traits were moderate to low that indicated the low progress with direct selection.

Fertility is considered an important economic trait in cattle, yet despite its importance, reproductive efficiency of dairy cattle has decreased dramatically in the past decades and is of increasing concern to farmers and the dairy industry (Ghiasi et al. 2011; Pryce et al. 2010). The decline in the reproductive efficiency of dairy cattle has become a challenging problem worldwide. Genetic response for fertility traits is expected to be small due to low heritabilities as shown in many studies. In the past decades, more attention has been placed on milk production in selection programs worldwide, which has caused a decline in female fertility due to the antagonistic genetic relationship between milk production and fertility (Pryce et al. 2010; Toghiani 2012). Therefore, it is necessary to include fertility traits in the breeding programs for improving fertility or stopping its downward genetic trend (Penagaricano et al. 2012). It is well documented that bull fertility is influenced by genetic factors. Semen production traits, such as volume and sperm concentration, were found to have moderate heritabilities (from 0.15 to 0.30), whereas some of the semen quality traits, such as motility and percentage of abnormal sperm, had moderate to high heritabilities (close to 0.60) (Rezende et al. 2018). The objective of this study was to assess genetic parameters for fertility traits in Holstein dairy cows and sire conception rate (SCR) a new phenotypic evaluation of bull fertility.

In this study information's related to heifers and cows 1 to 3 parity, 1992 to 2018, by the National Animal Breeding center and promotion of Animal Products of Iran and sire conception rate (SCR) 2008 to 2018, a phenotypic evaluation of bull fertility, has been provided to dairy producers the Council of Dairy Cattle Breeding (CDCB) were used. Traits included: sire conception rate (SCR), age at first service (AFS), age at first calving (AFC), days open (DO), calving interval (CI), gestation length (GL), Pregnancy rate (PR), Interval between first and last insemination (IFL), Days from calving to first service (DFS) and number of services per conception (NS). Edited data included the following: SCR between 7.6 % to -16 %, AFS between 320 and 900 days, AFC between 630 to 1350 days, CI between 300 and 700 days, IFL less than 290, GL between 260 and 290 days, DFS between 20 and 300 days, DO between 40 and 350 days, PR between -28 and 55 and NS between 1 and 10. Select the model equations and fixed effects were optimized using GLM procedure in SAS package. Subsequently, the multi-variate animal model analyses was carried out in order to estimate of direct additive genetic and phenotypic correlations between fertility traits. Estimation of genetic parameters using animal model in REML methodology was done by REMLF90 program.

Heritability estimates for all fertility traits were low, minimum heritability were estimated for IFL trait by heifers (0.002) and maximum amount for SCR trait (0.303). Heritability were estimated for other fertility traits 0.033, 0.005, 0.052, 0.048, 0.085, 0.108, 0.095, 0.059, 0.031, 0.025, 0.023, 0.017, 0.018, 0.013, 0.009, 0.013, 0.032, 0.028 and 0.018 for AFS, AFC, CI, OD, PR, GL_H, GL, DFS_1, DFS_2, DFS_3, IFL_1, IFL_2, IFL_3, IFL_123, NS_H, NS_1, NS_2 and NS_3, respectively. These estimates are in agreement with the results obtained by Ghiasi et al. (2011) and Rahbar et al. (2016) in Holstein cows. Heritability estimates obtained in this study were larger than the ones obtained by Toghiani Pozveh et al. (2009) for CI, DFS and DO in the previous study of Iranian Holsteins. The heritability estimates obtained for interval traits (DO, CI, and GL) were higher than those obtained for categorical (NS) or binary traits. However interval traits may be affected by management decisions such as the length of the voluntary waiting period or estrus synchronization applied in some farms (Ghiasi et al. 2011). In general, negative and moderate additive genetic and phenotypic correlations estimates were obtained between fertility traits. Estimated additive genetic correlations in the range of -0.56 (between GL and AFC) and 0.83 (between IFL and NS). However, estimated phenotypic correlations in the range of -0.80 (between PR and OD) and 0.85 (between GL and OD). The first one is formed by the traits that measure overall fertility of the cow (i.e. CI, OD and PR) which can be obtained directly from calving dates. In particular, OD and PR showed additive genetic (-0.39) and phenotypic (-0.80) correlation estimates which indicates that these two traits are genetically the same as expected because PR is a linear function of OD. The same results were found by VanRaden et al. (2004) and Ghiasi et al. (2011) in Holstein cattle. The mean breeding values were estimated -0.31 to 0.38, -4.14 to -3.34, -6.70 to -5.64, -0.0435 to -0.0064, -2.20 to -1.93, 9.16 to 10.73, 8.72 to 10.15, 3.29 to 4.21 and 4.34 to 5.84 for SCR, AFC, AFS, GL, PR, CI, OD, IFL DFS, respectively. However, the mean phenotypic values was positive, except SCR (range -0.62 to 0.66). These results were in agreement with reported Aghajari et al. (2015); Ansari-Lari et al. (2009) and Shirmoradi et al. (2010). Were estimated negative genetic trend for SCR (-2.22), AFS (-4.02 %), AFC (-2.66 %), GL (-0.07) and PR (-0.83) traits. Subsequently, for CI, DO, IFL and DFS traits, positive phenotypic trends were obtained. These estimated genetic and phenotypic trend agreed with other reports (Faraji-Arough et al. 2011; Rahbar et al. 2016).

Genetic parameters (heritabilities and genetic correlations) have been estimated for fertility traits in heifers and cows and fertility bulls. The results of this study indicated that breeding programs have paid little attention to reproductive traits in Iranian Holstein cows, and therefore it is recommended to pay more attention to these traits in order to improve the performance of Holstein cows. According to the results suggest that the genetic prediction of dairy bull fertility is feasible. This could have a positive effect on the dairy industry, for example, the early culling of bull calves with very low SCR predictions.

The aim of this research was to assess the growth curve of Makuie lambs using four growth functions including Logistic, Gompertz, Von Bertalanffy, and Verhulst as well as estimation of the parameters of these functions. 14454 live body weight records (LBW) that were collected in Makuie Sheep Breeding Center during 1990 -2016 were used. The NLMIXED and NLIN procedures of SAS (version 9.4) were used for fitting and estimation of parameters. Different indices were considered for selection of the most appropriate model. The asymptotic mature weight was 25.93 to 36.8 kg for all animal, 32.46 to 25.23 kg for male lambs and 29.29 to 31.15 kg for female lambs, respectively. The highest and lowest growth rate was observed in Verhulst and Von Bertalanffy (0.021 and 0.011, respectively). The logistics function showed the high growth rate for males than females (M: 0.014, F: 0.013), whereas Gompertz function showed a high growth rate for females (M: 0.011, F: 0.013). In order to compare different models, correlation coefficient (R), coefficient of determination (R2), Bayesian information criterion (BIC), Akaike information criterion (AIC), the mean absolute deviation (MAD), residual variance index (Se2) and RMSE were used. According to these indices, the Von Bertalanffy curve was the appropriate model because it achieved the lowest values for AIC, BIC, RMSE, MAD and S2e indices and the highest values for R and R2. When data were analyzed based on sexuality, the accuracy of assessment increased and the models better fitted to the data. The results of this study demonstrated that Von Bertalanffy model could accurately describe the growth pattern of Makuie sheep, especially, when males and females were evaluated separately.

The purpose of this study was to compare the accuracy of genomic evaluation for Bayes A, Bayes B, Bayes C and Bayes L multi-step methods and SSBR-C and SSBR-A single-step methods in the different values of π for predicting genomic breeding values of the genotyped and non-genotyped animals. A genome with 40000 SNPs on the 20 chromosom was simulated with the same distance (100cM). The π values that maximized the prediction accuracies in BayesC were 0.980 and 0.995 for the normal and gamma distributions of QTL, respectively, and were also used in SSBR-C method. Genomic prediction accuracy in the SSBR-C (π = 0.99) method was higher than multi step methods from 0.02 to 0.09 for gamma distribution. Results showed that considering mixture distribution and use of phenotype, genotype and pedigree information simultaneously, the SSBR-C (π = 0.99) method had higher accuracy than other methods and is considered a better choice in this scenario. Moreover, both single and multi-step methods showed similar prediction accuracy when the genetic architecture appeared to approach the normal distribution. Furthermore, SSBR-C (π = 0) method appeared to be more reliable choice that was due to regressions of true breeding value on estimated breeding value close to one in normal distribution. Generally, GEBV accuracy decreased as the distance increased between validations and training set, which was more sensitive for non-genotyped individuals compared to genotyped individuals.

Ascites syndrome, also called pulmonary hypertension syndrome is a major concern of the poultry industry for decades. There have been a numerous reports regarding the underlying genetic variability of different breeds/strains in resistance against ascites. This scenario indicates gaining deep insight into the genomes of chicken pure lines, in order to find the favorable alleles within candidate genes controlling the variation of resistance/susceptibility to ascites. In this context, the aim of the present work to study the polymorphisms within STAT5B gene in the B line of Arian strain and to investigate the potential association of the confirmed polymorphism with body weight and growth rate traits at different ages as well as with carcass-related traits and ascites indicator traits. For this purpose, in overall, 166 birds, which their health status has previously been confirmed via clinical trials was chosen for genotyping the STAT5B gene in a polymorphic site using PCR-RFLP and MspI restriction enzyme. Enzymatic digestion results identified three AA, AG and GG genotypes with 0.46, 0.44 and 0.1 frequencies, respectively. Birds carried GG genotype were significantly (P<0.01) heavier in body weight (8-10%) or faster in growth rate (12-13%) than other genotypes all over of the rearing practice, particularly at 14-21 days of age. Furthermore, this genotype group was affected the heart weight considerably (P<0.10). However, there were no significant differences between the least square means of the remaining carcass related traits of the three genotypes. Moreover, the obtained outputs verified that there is no significant difference between the STAT5B genotype and allele frequencies between the healthy and ascites birds. Finally, it can be concluded that, the mutation of adenine to guanine substitution improves body weight and growth rate, but this polymorphism has nothing to do with ascites. This finding may emphasis to the fact that, despite the previous thoughts about the direct relationship of the genes controlling the ascites and growth, genes improving the body weight or growth rate do not have a direct influence in increasing the incidence of ascites. Further research is needed in order to be able to propose a general conclusion about the association of genes affecting the ascites and growth.

Object and The purpose of this study was to evaluate the polymorphism of two candidate genes including Leptin (exon 2 and intron 2) and growth hormone receptor (promoter) genes and their effects on semen quality of Holstein bulls.
For this purpose information from two breeding stations including the Animal Breeding Center of Northwest (41bulls) and the National Animal Breeding Center and Promotion of Animal Products (28 bulls) were used. Information of 69 bulls, which were sampled between 2003-2013 years was used. Four qualitative sperm traits including volume of ejaculation (mL), sperm population (million/mL), and percentages of live sperm before and after freezing (%) were measured. Polymorphism of leptin gene was evaluated by PCR-RFLP and the variation in the growth hormone receptor gene was evaluated by PCR-SSR.
The results of this study showed that all investigated loci were polymorphic and the frequency of TT, AT, AA genotypes in exon 2 were 0.28, 0.20 and 0.52, and frequency of T and A alleles, were 0.62±0.041 and 0.38±0.041, respectively. Additionally, genotype frequencies for AA, AB, and BB genotypes in the intron 2 were estimated 0.68, 0.32 and 0.16 and estimated A and B allele frequency were 0.84±0.031 and 0.16±0.031 respectively. For promoter of growth hormone receptor, results indicated that three genotypes of TG17/TG17, TG17/TG21 and TG21/TG21 had frequencies of 0.27, 0.48 and 0.25 and two alleles of TG21 and TG17 had frequencies of 0.49±0.042 and 0.51±0.042, respectively. There was Hardy-Weinberg equilibrium for the promoter of growth hormone receptor (P Generally, there was a significant association (P

This research was carried out to estimate genetic and phenotypic parameters for growth traits of Mehraban sheep. The data included the birth weight (BW، n=6645)، 3-month weight (3MW، n=3685)، 6- month weight (6MW، n= 2406) and 9-month weight (9MW، n=1684) records that were collected from 1994 to 2010 at Mehraban Sheep Breeding center under supervision of Agriculture-Jahad Organization of Hamedan. The pedigree file consisted of the information of animals from 1987 to 2010 years of birth. The effects of herd-year، season، sex، type birth and age of dam at lambing were fitted in the model as fixed effects. Due to the variation between ages at weighting of lambs this effect considered as a covariate in the model. The significant of fixed effects were determined using general linear model (GLM) procedure of SAS software. Bayesian analysis was carried out for traits، using the GIBBSS3F90 program. The most suitable model for each trait was determined based on deviance information criterion (DIC). The estimated direct heritabilites with most appropriate model were 0. 24، 0. 43، 0. 43 and 0. 23 for BW، 3MW، 6MW and 9MW traits، respectively. The estimated maternal heritabilites were 0. 04، 0. 43 and 0. 19 for BW، 3MW and 9MW، respectively. The results of this research show that a fair rate of genetic progress seems possible by selection for all traits، but negative genetic correlation was observed between direct and maternal genetic effects for all the traits، indicating antagonistic pleiotropy، which needs special care while formulating breeding plans.

In order to estimate the genetic parameters and comparison legendre polynomials for production traits of Iranian Holstein dairy cattle test-day records of first lactation cows for milk yield, fat yield, protein yield, fat percentage and protein percentage traits were used. These records collected from 2006 to 2010, by the Animal Breeding center of Iran. The genetic parameters were estimated using REML algorithm by random regression model with legendre polynomials of order 3 to 5. According to the results, order 5 of legendre polynomials were proposed for fitting permanent environment and additive genetic effects for the milk yield, fat and protein percentages traits, and order 4 of legendre polynomials for the fat and protein yields. Residual variances were considered homogeneous over the lactation period. The Phenotypic variance of the considered traits during lactation was not constant and it was higher at the beginning and the end of lactation. Additionally, the estimated permanent environment variance was larger than genetic variances throughout the lactation. Estimates of heritability were found to be lowest during early lactation. The additive genetic correlation between lactation adjacent days were more than between distant test days. The genetic trend of milk yield was showed an increasing phase during the10 past years, while this genetic trend for fat and protein percentages declined.

The aim of this study was comparison of restricted maximum likelihood and Bayesian statistical methods to estimate of (co)variance components of production traits of Iranian Holstein cows by multi-trait random regression animal model. Data included 181,226 test-day records collected on 21,469 fist lactation cows. The model included herd-year-season of calving, year-month of test day, somatic cell score, age of calving and milking time as fixed effects, and random regression coefficients for additive genetic, and permanent environmental effects. For restricted maximum likelihood method, heritability's of test-day milk yield were higher in the middle (0.30) than at the beginning (0.08) or the end (0.23) of lactation, but for Bayesian method the highest value was observed for end of lactation (0.31). Genetic correlations among 305-d yield traits ranged from -0.502 (milk yield-protein percentage) to 0.712 (fat-protein percentage) for restricted maximum likelihood method and from -0.525 (milk yield-protein percentage) to 0.703 (fat-protein percentage) for Bayesian method. Residual variances of production traits for first method were 0.005 (%2) to 0.64 (kg2) higher than these values for second method.

(Co) variance components of milk, fat and protein yield of 11368 first lactation Iranian Holstein dairy cow were estimated by single trait random regression test-day models and restricted maximum likelihood (REML) method. In this study the homogeneous residual variance was assumed throughout lactation. Estimated heritability for 305-day production traits ranged from 0.25 to 0.31 and the highest genetic correlation between different days in milk was observed for protein yield. Persistency is one of the key factors determines the amount of total milk yield during a lactation period. Persistency is heritable economic trait and an important parameter of lactation curve in dairy cattle. In this research five different persistency measures were included: 1) different between estimated breeding values (EBVs) from d 290 and d 90; 2) different between EBVS from d 1 to 100 from d 101 to 200; 3) different average of EBVs from d 255 to d 305 from d 50 to d 70; 4) summation of different EBVS for d 61 to 280 from d 60; 5) summation of the EBVS from d 60 to 279 as a deviation from d 280, were used. Heritability’s as a function of time were calculated. Heritabilities estimated for persistency of protein yield (6-31 percentage) were slightly higher than this values for persistency of milk (7-28 percentage) and fat (3-25 percentage) yield, as well as genetic correlations between persistency and 305-day production were estimated slightly higher for protein and milk yield than thus for fat yield.

The present study was carried out to estimate genetic and phenotypic parameters for litter size trait in Mehrabani sheep by threshold model. Data file included the litter size of 5069 ewes which collected from 1994 to 2010 at Mehrabani sheep breeding center under supervision of Agriculture-Jahad Organization of Hamedan. The pedigree file consisted of the information of animals burned which were born from 1987 to 2010. The number of lambs per ewe were used as, repeated records. The effects of herd, year, and lambing time were fitted in the model as fixed effects. The significance of fixed effects was examined using Logistic procedure of SAS software. The analysis for trait was carried out, using the Thrgibbs1f90 program. Estimates of direct heritability, maternal heritability and repeatability were 0.039, 0.11 and 0.057, respectively. The low estimations of direct heritability, maternal heritability and repeatability were obtained in the current study for litter size trait. It indicates that selection based on the ewe’s own performance may result in slow genetic improvement.

The existence of genotype by environment interaction on milk production traits of Iranian Holstein dairy cattle was investigated by considering recorded performance of daughters of sires at different calving seasons regards as different traits. In the current study، recorded data on Holstein cows between 2001 and 2010 were used. For production traits (milk yield، fat percentage and protein percentage)، genotype by environment interaction were estimated by using multiple-trait random regression sire model. Additive genetic and permanent environmental variances of milk production traits did not vary at different calving seasons. Heritabilities for 305-d production in spring، summer، autumn and winter calving seasons were 0. 26، 0. 29، 0. 27 and 0. 24 for milk yield، respectively; 0. 28، 0. 30، 0. 30 and 0. 28 for fat percentage، respectively and 0. 42، 0. 45، 0. 40 and 0. 38 for protein percentage، respectively. The genetic correlations for 305-d milk production traits between different calving seasons were higher than 0. 93 and that no significant genotype by environment interaction was indicated. The Spearman rank correlations between estimated breeding values of 20 top sires between summer and autumn and also between summer and winter were 0. 68 for protein percentage and the re-ranking of sires between these seasons were observed. In conclusion، results from this study indicated that there were interactions between genotype and season of calving for milk production traits in Iranian Holsteins dairy cows. The results of current study can be used to increase the accuracy of sire selection.