محمد رزم کبیر

There has been extensive ongoing research on honey bees over the last 100 years, mainly due to their vital role in society, particularly in agriculture, economics, food security, medicine, and even computing technologies. Our knowledge is growing faster than our ability to absorb it. The most common and prominent honey bee species in Iran is the western honey bee, Apis mellifera, which includes the Iranian honey bee, Carniolan, and Iranian × Carniolan hybrids. The objective of this study was to identify the effects of crossbreeding on population dynamics, egg-laying rate, propolis production, honey yields, pollen collection, and defensive behavior in Iranian (Apis mellifera meda) bees. In early March 2021 (the end of the colonies' wintering season), eight colonies of each of the three bee breeds were randomly selected and standardized based on honey and pollen storage, the number of brood frames, initial population, and hive type (Langstroth hives). The tested colonies did not receive any chemical treatments during the experiment. Data collection and recording of egg-laying rates and brood rearing were conducted weekly during spring 2021 at the Honey Bee Research Centre, University of Kurdistan, Sanandaj, Iran (35.27788; 46.99562). Data quality control and analysis were performed using the SAS program. The results showed that Iranian × Carniolan hybrids were more efficient than the parental stocks in terms of population dynamics and pollen collection (p<0.05), indicating positive heterosis and complementarity between the breeds. Carniolan (Apis mellifera carnica) bees demonstrated significantly favorable performance for honey yields (p<0.05). Iranian (Apis mellifera meda) bees were superior in propolis production and exhibited higher levels of aggressiveness, significantly (p<0.05). Heterosis was estimated based on the superiority of observed phenotypes in the hybrids relative to their inbred parents. The estimated heterosis values were -3.8% for honey yields, 32.6% for pollen collection, 10.0% for egg-laying rate, 24.5% for population growth rate, -2.1% for propolis production, and -16.3% for aggressiveness. The highest level of phenotypic diversity was observed in the Iranian honey bee, suggesting the potential benefits of this breed for selection programs. Additionally, the findings revealed that the controlled use of higher-performance lines, either in pure or hybrid forms, will improve the efficiency of honey bee colonies and is recommended.

The aim of this research was to study different economic selection indices to increase body weight in Markhoz goat breed. Body weight (BW) at different ages (birth, weaning, 6-month and 9-month) were categorized as several two- and three-traits selection indices. Genetic parameters were estimated with MTGSAM using the Bayesian statistical method. Selection index analyzes were done using SelAction software. The results of comparing three-trait indices showed that highest total economic gain resuled from I9 which was US$4.86. The total economic response for two-trait index belonged to I4 which was US$3.94 and higher than 5 others. The highest direct genetic gain from three-trait indices was predicted for 9-month weight in I8 and I9 indices to be about 0.63 kg. In addition, the highest direct genetic improvement resulting from two-trait indices was also predicted for the 9-month weight in the I3 to be 0.66 kg. Moreover, the selection and performance criteria revealed decrease in phenotypic variance, heritability, and genetic correlation of traits. These changes differed in alternative selection schemes, which influenced by the initial population parameters, selection intensity, direct or indirect selection, and the number of traits included in the selection index. In conclusion, to maximize the total economic gain, two selection indices I9 and I4 can be suggested for the current condition of the Markhoz goat population. However, to preserve the phenotypic/genetic variance of traits, it is necessary to focus on strategies such as selection intensity, economic coefficients, indirect selection, and increasing the number of traits in selection indices.

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.

Selective signatures provide information about the stages of evolution of different species, including sheep, which lead to changes in their genome over many years. The aim of this study was to detect signatures of selection in the genome of Iranian sheep in the highlands. A total of 58 sheep from 4 breeds, two breeds scattered in the highlands and two breeds scattered in the lowlands, were genotyped using Illumina ovine SNP600K BeadChip genomic arrays. Two statistical tests of unbiased FST (Theta) and hapFLK were used to identify the selection signatures. The results of Theta revealed 22 genomic regions and the results of hapFLK revealed 15 genomic regions. Bioinformatics analysis demonstrated that many of genes had important effects on adaptation to hypoxia conditions e.g. rheumatoid arthritis (TXNDC5), hematopoiesis (FANCA), immunity and fighting infection (LEF1, NMUR1, PTMA and COPS7B), regulating blood pressure and responding to pain and inflammation (NMUR1) and suppressing cancer (CD82, GAS8, PRMT1, B3GNT7). For example, TXNDC5 and FANCA functional genes, which are located on chromosome 13 and 14, were related to reacts to hypoxic conditions and hematopoiesis. Also, genes such as LEF1, NMUR1, PTMA and COPS7B are effective in immunogenicity and fighting infection. Study of the reported QTL in these regions of the sheep genome showed that they overlapped with QTL of economically important traits such as corpuscular hemoglobin concentration, hematocrit, traits related to meat, carcass, milk, body weight, bone density, and the total number of lambs born. Due to the fact that little research has been done regarding adaptation to altitude in sheep, the results of this research may facilitate the identification of genes affecting adaptation to altitude. However, it will be necessary to carry out more association and functional studies to demonstrate the implication of these genes.

The principal aim of the sheep industry worldwide is to produce high-quality meat. In addition to the meat, milk, and wool production are the economic traits in sheep breeding programs. Wool production is one of the most important economic characteristics of sheep with a complex physiological and biochemical process that is influenced by genetics, environment, and nutrition. Almost all Iranian sheep breeds are double-coated and produce carpet wool. Therefore, considering the role of wool on the country's economy, it is necessary to conduct a study to identify the genetic factors affecting this trait. Identifying the genomic regions under selection is effective in understanding the processes involved in the evolution of the genome and also in identifying the genomic regions involved in the emergence of economic traits. Selective signatures in the whole genome can help us to understand the mechanisms of selection and to identify the genomic regions that have been under natural or artificial selection for many years. Since Selective signatures are usually associated with major effect genes and important economic traits, they can provide suitable information sources to improve the performance of selection programs in the future. The objective of this study was to identify the genomic regions that have been under selection in skin and wool sheep breeds.

 In the present study, Illumina ovine SNP600K BeadChip genomic arrays of 80 sheep from six breeds were used, three breeds were bred for their skin (Karakul, SiahKabud, and Gray Shiraz) and three breeds were bred for their wool (Sanjabi, Kermani and Baluchi). Unbiased methods of Weir and Cockerham’s FST (Theta) and hapFLK were used to detect the selection signatures. Also, to check the genes and QTLs in the selected regions, the Biomart database, OAR 3.1 version of the sheep genome, was used, and the function of the identified genes was analyzed through a wide search in different databases such as Genecards and OMIM. Finally, the list of genes related to the selected regions was reported. For this purpose, the chromosomal position of SNPs with high numerical values of theta and hapFLK, as well as the 250 kbp region around these markers, were further investigated. Then, the DAVID database online search was used to investigate the biological and functional processes of genes and to study the ontology. Finally, Cytoscape software was used to determine gene networks.Then, DAVID database online search was used to investigate the biological and functional processes of genes and to study the ontology.سپس از جستجوی آنلاین پایگاه داده DAVID برای بررسی فرآیندهای بیولوژیکی و عملکردی ژن ها و مطالعه هستی شناسی استفاده شد.Then, to investigate the biological and functional processes of genes and to study the ontology, DAVID database was used to search online. سپس برای بررسی فرآیندهای بیولوژیکی و عملکردی ژن ها و مطالعه هستی شناسی از پایگاه داده DAVID برای جستجوی آنلاین استفاده شد. Can't load full results Try again Retrying…

 The results of the Theta analysis revealed 26 genomic regions on 1, 2, 6, 19, and 24 chromosomes, and the results of hapFLK revealed seven genomic regions on 6 and 19 chromosomes. Bioinformatics analysis demonstrated that some of these genomic regions overlapped with known genes related to pigment traits and characteristics of wool (KIT on chr 6, MITF on chr 19, IGSF10 on chr 1, PDGFRA on chr 6), muscles (MICALL2 on chr 24), vasodilation and immune response (P2RY on chr 1) and cancer (MIR339 on chr 24, ELFN1 on chr 24, MAD1L1 on chr 24, GPR87 on chr 1). The investigation of reported QTLs showed that these regions are related to QTLs of important economic traits, including traits related to meat, carcass, milk, body weight, bone density, and the total number of lambs born. Also, the analysis of Gene Ontology and Enriched pathway terms in regions under positive selection were related to the pathways involved in the differentiation of melanocytes and pigments, differentiation of stem cells, cellular processes, development of the immune system, blood system, reproductive and cellular processes. The results of the gene networks with the information obtained from Theta and hapFLK statistics showed that the genes identified were significantly active in the development and morphogenesis networks of the embryonic digestive tract, the networks related to pigment and melanocyte differentiation, and the networks related to Purine and G protein. However, to identify the exact function of the identified genes and QTLs, it is recommended to carry out more investigations.نتایج شبکه های ژنی با اطلاعات به دست آمده از آمار تتا و hapFLK نشان داد که ژن های شناسایی شده به طور معنی داری در شبکه های رشد و مورفوژنز دستگاه گوارش جنینی، شبکه های مربوط به تمایز رنگدانه و ملانوسیت و شبکه های مربوط به The results of the gene networks with the information obtained from theta and hapFLK statistics showed that the genes identified were significantly in the development and morphogenesis networks of the embryonic digestive system, the networks related to the differentiation of pigment and melanocytes and the networks related to نتایج شبکه های ژنی با اطلاعات به دست آمده از آمار تتا و hapFLK نشان داد که ژن های شناسایی شده به طور معنی داری در شبکه های رشد و مورفوژنز دستگاه گوارش جنینی، شبکه های مربوط به تمایز رنگدانه ها و ملانوسیت ها و شبکه های مربوط بهCan't load full results Try again Retrying…

 The results of the present study and the identified genomic regions can play an important role in the study of the effect of the selection on population differentiation in two sheep breeds that bred for skin and sheep production. Subsequently, this would direct us to identify the genomic regions associated with traits that differentiate these groups. However, these areas need to be confirmed in other independent studies with more samples. In general, the data of this research can be used in research related to genomic selection, design of mating systems, and additional reviews and evaluations to improve skin and wool production in sheep.

Natural and artificial selections change the allele frequencies among populations and consequently leave detectable patterns on the genome during domestication. A little information about the differences of genetic structures of Iranian native breeds that result in these selective pressures are available. Therefore, in this study, we examined genetic diversity using four native horse breeds including Turkmen (29 samples), Caspian (21 samples), Kurdish (67 samples) and Persian Arabian (52 samples).

For this purpose, three methods including Principal Component Analysis (PCA), neighbor joining (NJ) and admixture were investigated. In addition, with the integrating of three different selection of signature methods, including TajimaD, nucleotide divergency (pi) and integrated haplotype homozygosity score could identify the selection traces in these breeds. To integrate the different results of these three methods, De-correlated composite of multiple signals framework was used.

All the methods used to examine the genetic structure well separated these four breeds and showed a pattern related to geographical origin for their genetic diversity. By combining different methods of identifying the selection signatures, many genes affected by the selection pressure were identified in all four breeds. These genes were associated with specific GO terms and QTL, in which 16 shared candidate genes among all four breeds were identified as suggested candidates for selection. Additionally, 11 different QTL types was identified in all four studied breeds, which was divided into the category of traits related to adaptation, fitness through genetic disorders or morphological and behavioral traits.

Overall, the results of this study can help better understand the process of natural and artificial selections in Iranian horses. In addition, this research has helped to improve our understanding about the genetic differences between studied Iranian horse breeds, which can help in finding the best solution to preserve and improve genetic diversity.

The profitability of dairy cattle herds mainly depends on the reproductive performance of cows. Because reproductive performance is effective on the amount of daily milk and calf production per cow, increasing the number of inseminations per conception, lengthening the calving interval in the herd, and causing the voluntary (due to the age of the animal) and involuntary (due to illness or fertility disorders) elimination. For evaluating fertility traits, we sometimes come across domains whose records were incomplete at the time, out of range, or delayed. These records are called censored records. The typical way to deal with such records is to delete them from the data set. However, this reduces the information available for genetic evaluation and may affect trait variance by masking actual genetic differences between animals. Another way is to use appropriate statistical methods to place censored records in genetic evaluations. The use of censored records in genetic evaluations yields more reliable genetic parameters, and it can increase the estimation accuracy of breeding values giving the support that the breeding value estimation is close to the true breeding value. The present study aimed to investigate the role of censored records in the estimation of genetic parameters of the number of inseminations per conception in Iranian dairy cows.
The dataset included 29621 records collected from 2005 to 2019 by the Vahdat Cooperative of Isfahan in Iran. FoxPro software (version 9.0) was used to edit and prepare data, remove incorrect records and create contemporary groups. Five animal models, including Threshold Model (TM), Penalty Model (PM), Modified Penalty Model (MPM), Threshold-Threshold Model (TTM), and Modified Threshold-Threshold Model (MTTM), were used for the genetic evaluation of this trait. The predictive ability of the models was assessed using cross-validation. Also, a simulation study was performed to determine the appropriate model.
The estimated heritability using different models was 0.3 (0.013), 0.07 (0.013), 0.07 (0.013), 0.3 (0.13), and 0.4 (0.012) for TM, PM, TTM, MPM, and MTTM, respectively. The highest correlation of breeding values for all animals was between MPM and TM (0.98). The same result was obtained for the best sire (sire with at least 20 daughters). The high correlation shows that these models work similarly in selecting the best sires. The low correlation between models indicates that the models rank sires differently. Therefore, the choice of model can affect the ranking of males. The top 10 sires showed less correlation between models than the active sires. When the number of males decreases, the selection becomes more strict. The prediction accuracy in models with censored records is higher than in models without censored records because the use of models that include censored records is due to the increased information about the trait, and the absence of bias due to deleting records improves the accuracy of estimating breeding values. Consequently, if the censoring status is not taken into account, animals with superior breeding values may be biased. The low ratio of common superior animals in different percentages between models showed that these two models rank the animals differently. Still, the high proportion of common superior animals indicates the similarity of the two models in choosing the best animals. So, model selection can be effective in ranking superior animals because when the censored record is used, the information is increased and the situation would be closer to reality. The cross-validation showed that the accuracy of the breeding value prediction by different models was higher in models with censored records than in models without censored records. The results of the simulation study showed that the models with censored records provided better genetic parameter estimates. TTM has been able to estimate expected heritability better than other models. The percentage of top animals in common between the top 10 and the top one percent of simulated animals with different models showed that the ability of the TTM model to select real top animals is more than other models, which shows that this model has identified top animals better.
The results of the present study showed that the censored records are important for estimating the genetic parameters of the number of inseminations per conception in Iranian dairy cows and ignoring those led to a biased estimation of the parameters. Data simulations also showed that using censored records provided more realistic genetic parameter estimates.

Milking activity is one of the most important daily activities in industrial dairy farms, which accounts for about 80 percent of the annual milking costs and more than 50 percent of the daily activities of each dairy farm. Milking speed (MS) is an important functional trait to dairy producers and in few countries (e.g. Canada), MS have been recorded for several years by milk recording agencies. Functional traits include traits related to animal health, feed efficiency, milkability, and calving ease. Milkability is the ease of milking in dairy cows (Gäde 2007). One of the important traits related to milkability is milking speed and usually, milkability is evaluated by measuring this trait (Gray et al. 2012). Milking speed refers to the amount of milk released from the mammary glands per minute (Klindworth 2003). Due to the effect of milking speed on udder health and labor productivity, this trait is considered a very important one from an economic point of view and has been emphasized in dairy cow selection programs for many years (Karacaören et al. 2006). However, there is evidence that increasing of milking speed is associated with increasing of udder health problems such as mastitis, and as a result, the optimal milking speed is considered by breeders. Milking speed can be assessed based on two indicators; 1) Scoring based on 1 to 5 points and 2) Using electronic stopwatch and flowmeter. The advantage of using a stopwatch is that it is being used easier than a flowmeter and the duration of its use is being reduced, However, the need for a technician to be present during milking is a major drawback of this method (Beard, K. 1993). This study aimed to estimate genetic parameters for milking speed and association between milking speed and milk components (somatic cell score, protein percentage, and fat percentage), in Holstein dairy cows in Isfahan province, Iran.

Data were 5292 observations related to 1762 cows in 7 herds of Holstein dairy cows in Isfahan between October 2015 and April 2016. The distribution of MS scores was skewed toward faster milking speeds. Milk components data, including fat percentage, protein percentage and somatic cell count, belong to the same date, was obtained from the Isfahan Farmers' Cooperative (Vahdat). Only herds with complete pedigree information and three milkings records per day were selectected for final dataset. The milking time of each animal was measured by using a stopwatch and recorded in the form. Data editing quality control was performed by using Excel (2013) and FoxPro 9.0. The average milking flow rate criterion(amount of produced milk in kilograms divided by the milking time in minutes) was used to evaluate the trait of milking speed. To generate data with normal distribution, the somatic cell count (SCC) was transformed to somatic cell score (SCS) by natural logarithmic conversion. Pedigree was traced back to the founder generation. (Co)variance components were estimated using a DMU software package with an average-information (AI)-REML algorithm for the analysis of multivariate mixed models (Madsen and Jensen, 2013).Genetic evaluations and Best Linear Unbiasd Prediction (BLUP) of breeding values for milking speed was computed with the animal model by DMU program.

The results of the analysis of variance showed that the effects of herd, milking frequency, parity, and stage of lactation on milking speed were significant (p <0.01) and remained in the model. However, the effect of age at the first calving on this trait was not significant (p > 0.05). Mean of milking speed of the animals in this study was 1.96 ± 0.75 kg/min, and the least square means of milking speed in the population was 2.11 (±0.01) kg/min. The highest least square means of milking speed belonged to the third-parity cows with a mean of 2/21 (±0.01) kg/min and the least square means of milking speed belonged to the primiparous cows with mean of 1/98 (±0.02). Older cows have higher-capacity gland cisterns, and consequently, their cisterns can store more milk than the cisterns of primiparous cows. More milk stored in the cisterns can put more pressure on the teat sphincter and escape more quickly. By stages of lactation, the highest least square means of milking speed was observed in group 4 (the cows in the 51 to 65 DIM) with mean of 2/32 (±0.05) kg/min which can be due to the location of the animal in its milk yield peak. Also, the lowest least square means of milking speed was related to group 15 (the cows in the 321 to 350) with mean of 1.79 (±0.04) kg/min. In late lacataion stage, the amount of milk produced by the cow will reach the lowest possible level during its lactation stage. The estimated heritability of the milking speed in this study was 0.22 (±0.06). The results of this study showed that the traits related to milkability have moderate heritability. A high genetic correlation was observed between milking speed and milk yield (0.90) and milking time (-0.83). The estimated genetic correlation between milking speed with fat percentage and protein percentage was high and negative, -0.69 and -0.47, respectively. In this study, the estimated genetic correlation between milking speed and somatic cell score showed that selecting to increase the milking speed would reduce the somatic cell count. In general, Genetic evaluations for MS can provide useful information for breeding decisions because of the moderate heritability of MS.

Milking speed has a moderate heritability that allows selection to improve this trait in breeding programs.

This study aimed to compare different methods of genomic evaluation using the criteria of correlation (ρ), regression (β), mean square error (MSE), and selection effectiveness (SE). For this purpose, nine different scenarios were designed based on different levels of heritability (0.1, 0.3, and 0.5) and different numbers of QTLs (20, 200, and 1000). To simulate different scenarios, five generations with a size of 1000 animals were simulated, of which the first two generations were considered as the reference population and the next three generations as the validation population. For each animal, a genome of 500 centimorgans with a marker density of 10000 SNP consisting of five chromosomes was simulated. Genomic breeding values were predicted using three statistical methods GBLUP, Bayes A, and Bayes B. The results showed that with increasing generation interval from the reference population, the accuracy of genomic breeding values decreased for three statistical methods, although Bayesian methods performed better in terms of continuity of accuracy. Based on the criteria of correlation, regression, and selection effectiveness, with increasing the levels of heritability, improved accuracy was observed, but the criterion of mean squares error showed the opposite trend. Bayesian methods performed better in low QTLs, but differences in different genomic evaluation methods were minimized in high QTLs. The results of selection effectiveness in comparison with the accuracy of genomic breeding value showed that accuracy can’t always be a suitable criterion for determining the superior method of genomic evaluation.

The objective of the present study was to perform genetic evaluation of dystocia, using linear (observed and adjusted) and threshold models in Holstein cattle. Data and pedigree information of 8 dairy herds were obtained from Vahdat Industrial Agriculturists & Dairymen Cooperative, Isfahan, Iran. Final data included 133876 calving records during 2005 to 2018. The fixed effects of the model were included, herd, year-season of calving, calf gender and age at first calving.  The random effects of the model were included sire, maternal grandsire, permanent environment of dam and residual effects. Furthermore, 305-day milk yield was considered as a covariate in the final equation. Quality control and data validation were conducted in SAS and Microsoft Excel. Genetic evaluations and prediction of breeding values for dystocia was computed, using different statistical models by DMU program. Direct heritability for dystocia for heifers and other cows based on linear model were 0.10 and 0.07 and based on threshold model were 0.13 and 0.10, respectively. Estimated heritability was higher in threshold model compare to the linear model. The results of this study showed that beyond the environmental improvement, genetic selection might be an option for decreasing dystocia. Estimated heritability for heifers and other cows by the linear model were adjusted on the underlying scale to 0.19 and 0.14, respectively. Estimated correlations between direct and maternal genetic effects were negative and ranged from -0.56 to -0.74, indicating the genetic antagonism between direct and maternal effects. The Spearman rank correlations for breeding values predicted from the linear and threshold models were significant and different from 1, indicating that ranking of animals are not unique in linear and threshold models. Based on the Akaike Information Criterion (AIC), threshold model was better and more accurate than linear model for genetic analysis of dystocia in Holstein cows.

Genetic parameters for birth weight and survival traits were estimated using data collected from 1995 to 2009 in Jafarabad Moghani Sheep Breeding Station. Number of observation for survival and birth weight were 7202 and 6585, respectively. Genetic parameters were estimated using restricted maximum likelihood (REML) procedure under 16 different models, including animal, threshold and sire models using ASReml software. The most appropriate model for each trait was determined based on Akaike’s Information Criterion (AIC) method. The appropriate model for birth weight was included direct additive genetic, maternal additive genetic and common litter effects. Also, the animal model for lamb survival were included direct additive genetic, maternal additive genetic, maternal permanent environmental, common litter effects and covariance between direct and maternal additive genetic effects. The appropriate sire model for lamb survival was included sire direct additive genetic and common litter effects. Estimated direct heritability for birth weight was 0.09±0.02. The direct heritabilities for survival with animal model and sire model was 0.070±0.002 and 0.05±0.02, which after correction was changed to 0.22 and 0.15, respectively. Also, the estimation of heritability for lamb survival from threshold model was 0.30±0.10. Genetic, environmental and phenotypic correlation between birth weight and lamb survival were 0.61±0.24, 0.08±0.02 and 0.07±0.01, respectively. Consideration of environmental factors, including common litter effects, is essential for unbiased estimation of genetic parameters.

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 aim of the present study was to investigate the effect of imported semen under different selection indices on phenotypic changes of production traits in Holstein cattle population in various climates of Iran. In the present research, records of first lactation on milk yield, fat and protein percentage of 269786, 216850 and 270566 Holstein cattle derived from imported semen under different selection indices in cold, semi-cold, moderate and warm climates, were used. The data were collected by the Center of Breeding and Improvement of Livestock Production during 1993 to 2017. The data were analyzed using the General Linear Model (GLM) software SAS 9.2. In cold, semi-cold, moderate and warm climates, the means of milk yield (kg) in the progenies of imported semen were estimated, 9597.92, 9654.46, 9718.34 and 8985.37, the means of fat percentage were estimated, 3.13, 3.07, 3.08 and 3.15 percent, and the means of protein percentage were estimated 3.10, 3.25, 3.14, and 3.16 percent, respectively. In the present study, the highest least square means of the phenotypic value of Holstein cattle derived from imported semen based on different selection indices in cold, semi-cold, moderate and warm climates were according to countries of France 2001-2011, Italy 1993- 2001, Netherlands 2001-2006, France 2001-2011 for milk yield, Netherlands 1999-2000, United States 1987-1988, United States 1992-1993, and Canada 1998-2000 for fat percentage and Canada 1991-1992, France 2012-2013, Sweden 2008-2012 and Germany 2002 -2007 for protein percentage, respectively.

The aim of this study was to compare three selection strategies and two properties of breeding value estimations using computer simulation. Simulated traits were weights at birth (BW1), eight weeks (BW8), twelve weeks (BW12), maturation (BWM) and also age at first laying (AFL), weight of first egg (EWM), average egg weight (EW) and egg number (EN). The first strategy was to select cockerels based on breeding value of BW12 and selection of hens based on a selection index with four traits including BW12, AFL, EW and EN. In the second strategy, cockerels and hens were selected using a selection index, as already said. But in the third strategy, cockerels were selected based on breeding value of BW12 and hens based on breeding value of EN. The individual's breeding values for three schemes were estimated by BLUP and ssGBLUP. Matings were performed based on the optimal genetic contribution. The results showed that the total economic values of the first and second programs using ssGBLUP estimations were 450, 460 and 421, and using BLUP were 434, 349 and 418, respectively. The rate of true inbreeding coefficient for ssGBLUP estimations were more than BLUP estimations (0.083, 0.287 and 0.046 vs. 0.072, 0.116 and 0.024, respectively). The results showed that the first strategy for a breeding flock of broiler production, the second strategy for a dual-purpose flock for producing egg and meat and the third strategy for a flock to achieve the highest total economic value with the lowest possible rate of true inbreeding were desirable.

The purpose of this study was to evaluate traditional, genomic, gene-assisted traditional selection and gene-assisted genomic selection methods for introgression a major gene in sheep population to improve the litter size trait using computer simulation. In this regard, a trait with heritability of 0.1, including two chromosomes each with a length of 100 cM, was simulated. On chromosome 1, a single QTL as the major gene was created that accounted for 40% of the total genetic variance. This QTL was located in the position of 25.7 cM. In breed A and B, animals were selected based for favorable and unfavorable to create two breeds that has been fixed for opposite alleles. Using the gene introgression approach, the favorable allele in breed A was insert to breed B and the new population was evaluated for genetic gain, accuracy of evaluation, frequencies of favorable allele and inbreeding rate. After five backcrossing generations, genetic gain in GasGenomic and Genomic methods was 26 and 62 percent higher than GasClassic and Classic methods, respectively. The frequency of favorable allele after five generations in Classic, Genomic, GasClassic and GasClassic was 0.387, 0.778, 0.965 and 0.975, respectively. The results of this study showed that if the goal is only introgression a major gene into an indigenous breed, by using gene-assisted classic selection, the frequency of a major gene can also be increased as much as obtain by the gene-assisted genomic selection.

The objective of this study was to compare introgression and synthetic breed strategies for litter size trait improvement in sheep using computer simulation. For this purpose, a trait with heritability of 0.1, consisting of two chromosomes was simulated. On chromosome 1, a single QTL as the major gene was created that accounted for 40% of the total genetic variance. The effect of favorable and unfavorable alleles for the QTL was fixed after seven generations in both A and B breeds, respectively. The introgression and synthetic breed strategies were compared using Classical and Classical with gene-assisted selection (GasClassical) methods. The genetic gain in introgression and synthetic breed strategies using GasClassical method was 39% and 16% higher than that of Classical method, respectively. The mean of inbreeding coefficient in the fifth generation in introgression strategy was 0.049 and 0.077 using the Classical and GasClassical methods, respectively, and in synthetic breed strategy was 0.11 and 0.008, respectively. The results of this study showed that the GasClassical method in comparison with the Classical method led to an increasing frequency of favorable allele (major gene) and genetic gain in both introgression and synthetic breed strategies. However, the genetic gain for one percent increase in inbreeding in the synthetic breed strategy was greater than that of introgression strategy, and as a result, the synthetic breed strategy performs better than introgression strategy.

The aim of this study was to identify regulatory genes affecting mastitis in dairy cattle using DNA microarray data. To reach this goal, the gene expression data with the largest number of arrays pertained to GPL1221 Platform with accession number GSE24560 was extracted from the GEO database. For quality control of data, ArrayQualityMetrics package and for preprocessing of data, three step function in AffyPLM, an add-in package in R environment were used. After identifying differentially expressed genes, a Tabu search algorithm was used to determine regulatory genes using bnlearn package in R environment. The results of this study revealed the causative and regulatory role for BCL2A, CCL2, S100A12, AOX1 and MGP genes on expression of other genes in mastitis of dairy cattle. Gene anthology analysis, revealed significant differences in 7 groups of molecular function, 48 groups of biological process and 11 groups of cellular components. Also, the results of the enrichment of the gene expression data set showed that most of the differentially genes expressed in this study that were significantly (P<0.05) active in metabolic pathways (GO: 0009605, GO: 0002376 and GO: 0006954) involved in response to pathogens, immune response and response to inflammation in the mammary tissue of dairy cattle.

Pedigree analysis is a useful tool for the study of structure, genetic diversity and history of populations. This study was conducted to characterize population structure, estimation of inbreeding and effective population size in Markhoz goats using pedigree analysis. Data consisted of pedigree records of 5726 animals, collected between 1987 and 2016 by Markhoz goat Performance Testing Station. Computer programs, including CFC for descriptive statistics of pedigree, ENDOG for estimation of effective population size and generation interval and EVA for inbreeding trend were used in this research. The ratio of inbred animals was 54.3% of total population. The mean of co-ancestry and inbreeding coefficients in the population were 2.61 and 2.68, respectively, indicated that the observed inbreeding level is higher than expectation. Estimated effective number of founders was 85.65, represents unequal founder contributions in population. Undesirable trend for inbreeding could be due to high contribution of few ancestors in reproduction and low migration rate of animals in Markhoz goat station. The mean generation interval for total population and founders were 3.23 and 5.73 years, respectively, emphasizing the replacement of young bucks and does in the recent generations. Effective population size computed via individual increase in inbreeding was equal to 60.41 and was not far from the 50, a critical level proposed for an ideal population. The results indicate that the biodiversity of population has decreased. In conclusion this research supports the priority for conservation genetic strategy instead of selection programs in Markhoz goat.

Genetic evaluations are computed to assess the genetic merit of animals based on mixed model equations. An important assumption for setting up these equations is that all genetic relationships among animals are available and correct. The objectives of the present study were to estimate the effects of incomplete pedigree and paternity errors on genetic evaluation. Data and pedigree of 100 dairy herds were obtained from Animal Breeding Center of Iran. Final data edited included milk yield records from 302860 first lactation Holstein cows. DMU Trace program was used for tracing ancestors and creating the full pedigree of animals. To simulate incomplete and wrong pedigrees, different scenarios including 8, 12, 16, 20, 24 percent of paternal identification numbers were removed or replaced using R program. Breeding values for milk yield was predicted by animal model using DMU program. Spearman's rank correlation was estimated for superior animals in different scenarios using SAS software. Estimates of heritability for full, incomplete and wrong pedigrees were 0.29, 0.26 and 0.27, respectively. The results showed a high variation in ranking of animals and determination of superior animals (P<0.01). As an example, at 12% level scenario, Spearman's rank correlation of BVs predicted from full pedigree with incomplete and wrong pedigrees were 0.65 and 0.60, respectively. Selection effectiveness, defined as the ratio of common superior animals in alternative scenarios, was decreased by increasing the rate of misidentification and errors (P<0.01). Incorrect pedigree and misidentification of animals could reduce accuracy of breeding values and consequently bias in animals ranking.

The aim of the study was to compare two strategies in Mazandaran native fowls using computer simulation.  Simulated traits included body weight at birth (BW1), at eight weeks of age (BW8), at twelve weeks of age (BW12), at maturation (BWM), age at sexual maturity (AFE), weight of first egg (EWM), mean egg weight from 28 to 32 weeks of age (EW28-32) and egg number (EN). The first strategy was to select cockerels based on breeding value of BW12 and hens based on a selection index with 4-traits including BW12, AFE, EW28-32 and EN. The second strategy was to select cockerels and hens using a selection index based on a 4-traits including BW12, AFE, EW28-32 and EN. After 10 generations, the first strategy improved BW1, BW12, BWM, EW28-32 and EWM to 1.49, 573.81, 397.58, 3.96, and 3.75 grams and AFE and EN to -3.51 days and 2.09 eggs, respectively. After 10 generations, the gain for traits in the second strategy for BW12, BWM, AFE and EN was 415.78, 218.74 grams and -9.77 days and 9.45 eggs, respectively. At the end of the tenth generation increase of inbreeding in the first and second strategies was 0.048 and 0.070, respectively.  The results showed that the first strategy was suitable for a breeder flocks with the aim of chickens suitable for broiler production and the second strategy was suitable for a dual-purpose flocks with the aim of producing egg and meat.

In the present study the potential consideration of calf birth weight (BW) and gestation length (GL) as correlated traits for improving genetic evaluation of calving difficulty (CD) in Iranian Holsteins at first calving was studied applying data collected from 2002 to 2013 in 131 herds by Animal Breeding and Production Improvement center of Iran. The influences of direct and maternal additive genetic effects on each trait were tested and the most appropriate model for each trait determined. Considering the most appropriate model for each trait, predictive abilities of four genetic evaluation models of CD including univariate model of CD, bivariate model of CD and BW, bivariate model of CD and GL and trivariate model of CD, BW and GL were compared applying mean of squares and correlation between observed and predicted values. The obtained results revealed that the model included direct additive genetic and maternal additive genetic effects as random effects, without considering covariance between them, was the most appropriate model for estimating variance components of the considered calving traits. Comparisons of the models revealed that model included CD and BW had better predictive ability than univariate model included CD and the bivariate included CD and GL in terms of lower mean square of error and higher Pearson correlation between observed and predicted values of CD. There was no difference between bivariate model (CD- BW) and multivariate model (CD-BW-GL) in terms of predictive ability. Therefore, BW can be considered as a suitable correlated trait for genetic evaluation of CD at first calving in Iranian Holsteins.

The aim of this study was to extract genes and probes hub based on Bayesian networks on probe and gene transcriptomic data over different tissues in Bovine species. Using raw probe and gene transcriptomic data in each tissue, genes and probes with the highest expression variances were extracted and fitted to Bayesian networks. The hub genes and hub probes were identified using the ratio of in-and-out degree.The size of the Markov Blanket was different in different networks. This might be indicative of existing of substructures topology of aforementioned network on different tissues. Using gene based network, in muscle (CBR1 and LOC788826); in mammary of (NID2, COL5A2, LOC616942 and FXYD3); in liver (LOC100132279; MGC127133; MBOAT2; CLDN2; ANKRD1; IGFBP1; DGAT2; CKMT1, ISG15, CKMT1), and in the ovary (LOC286871 and INHBA) were extracted as hub genes. It was shown that the hubs were different in different tissues. These results can be used for more accurate bioassays of each tissue. Using probe based network, two genes BOLA-DQB and JSP.1 would have function in liver and uterus tissues. The results of this study can reduce the gap between phonemics-and Genomics distance on investigated tissues in bovine species.

Single step genomic selection method (SS-GBLUP) with combining genomic relationship matrix and pedigree relationship matrix and simultaneous use of all genotyped and ungenotyped animals has been used widely in estimating genomic breeding values (GEBV) in recent years. The purpose of the present study was to compare genomic selection accuracy of traditional BLUP, SNP-BLUP, G-BLUP and SS-GBLUP for carcass weight in beef cattle. Results showed that genomic prediction accuracy for the above methods were 0.77±0.11, 0.88±0.21, 0.87±0.23 and 0.95±0.25 respectively. Genomic prediction accuracy of SS_BLUP was 0.08, 0.07 and 0.18 higher than SNP- BLUP, G_BLUP and traditional BLUP. Mean squared error of above methods was 3.46, 2.21, 2.22 and 1.76 respectively. In general, the performance of single step method was better than other studied genomic evaluation methods. Moreover, the regression coefficient of corrected phenotype on GEBV was 0.81, 0.82 and 0.90 for SNP- BLUP, G-BLUP and single step method, respectively. Thus, the bias of GEBV for single step method was less than G-BLUP and SNP-BLUP.

With development of mixed models, lactation model (LM) was replaced with random regression model (RRM). However, the minimum number of required test day (TD) records per animal to replace LM with RRM is a challenge in genetic evaluations. In this study, 381,236 test day records of 44,117 first parity dairy cattle which collected by Animal Breeding Center of Iran were used from 2006 to 2016. Based on number of TD records per animal, cows were divided into five groups by restricting cows that presented at least 2, 4, 6, 8 or 10 test day records in the lactation. The rank correlation between predicted breeding values (EBV) for LM and RRM irrespective of number of TD records was relatively moderate (0.44) and the rank correlation between two models using at least 2 TD records was 0.71. When 10 top percent of cows were used for comparison of LM with RRM, the rank correlation decreased to 0.08. The correlation of EBV of cows with ≥2 TD records with cows with ≥10 records in RRM was 0.85. The mean of breeding values accuracy in RRM was 4% higher than LM. Overall, use of RRM has advantages over LM and it is suggested to use RRM with at least 2 TD records instead of LM for genetic evaluation of milk yield trait.