hasan hafezian

Identification of single nucleotide markers and different methods of genomic evaluation in the form of marker assisted selection at the genome level has led to considerable genetic progress in the economic traits of domestic animals. The success of genomic prediction is measured by its accuracy. Deterministic formulas determine the relationship between prediction accuracy and factors affecting prediction accuracy and therefore before running into genomic selection, it is possible to design an optimal program such as the appropriate size of the reference population to achieve the optimum level of selection accuracy. The aim of the present study was to evaluate the prediction of the accuracy of deterministic formulas and compare it with the accuracy of prediction of genomic breeding values in the simulated study.

Four deterministic formulas including Daetwyler et al formula, Goddard formula, Goddard et al formula and Rabier et al formula were used to predict the accuracy of genomic evaluation in different genetic architectures including different levels for heritability, reference population size and number of independent chromosome segments. The ShinyGPAS program was used to compare and plot the accuracy of prediction. In order to compare the performance of deterministic formulas with the accuracy of predictions in the simulated population, population simulations were performed using QMSIM software. For this purpose, in genome simulation, three levels of heritability of 0.1, 0.3 and 0.5 and two levels of reference population size of 1000 and 2000 individuals were considered and estimation of genomic breeding values was performed using Bayesian method A and Bayesian B using BGLR package in R medium

In low heritability, the highest prediction accuracy was observed in Goddard formula, which had the closest prediction accuracy (0.56) to the accuracy of genomic evaluation of simulated data estimated by Bayes A method (0.56). With moderate heritability (0.3), Goddard (0.74) and Rabier et al. (0.73) had the closest and most similarity to the accuracy of the simulated data. With population size increased from 1000 to 2000 individuals along with increasing heritability, the performance of deterministic formulas was closer to the accuracy estimated from simulation data by Bayesian methods and the most agreement was obtained in Goddard and Rabier methods. In the lower independent chromosome segments, the highest accuracy obtained by Rabier et al (0.860). With increasing chromosomal independent segments, the highest value of accuracy obtained by Goddard predictive formula.

The results showed that deterministic formulas have a good ability to predict the accuracy of genomic evaluation and their performance is linked to the genetic architecture. The results suggest that the predictions of accuracy, in general, using Goddard and Rabier formulas are more consistent with genomic estimation accuracy in the simulated data.

Growth curves are an approach to describe the growth over a specific period of time, so it would be useful to use nonlinear growth functions to measure and predict weight at different times of an animal's life and calculate production profitability. Growth curves actually summarize the relationship between age and weight into a limited number of interpretable parameters. The aim of this study was to determine the best nonlinear model describing the growth curve in Mazandaran native chickens. To achieve this goal, body weight data on different ages of native chickens (hatch, 8 weeks, 12 weeks and sexual maturity) for the first 21 generations collected at the Mazandaran Indigenous Breeding Center were used. Nonlinear models of Gompertz, logistics; Brody, Verhulst, Richards and VonBertalanffy were tested to describe the growth curve. Fitting of nonlinear models was performed using nonlinear least squares (NLIN) procedure of SAS software. To determine the most appropriate growth function and comparison between models, the coefficient of explanation (R2), corrected coefficient of explanation, Bayesian information criterion (BIC), Akaike information criterion (AIC) and root mean square error (RMSE) were used. Gompertz model with the lowest values for MSE, RMSE, AIC and BIC statistics was detected as the best model describing growth curve and Richards’s model as the most inappropriate model describing growth in Mazandaran native chicken. Correlation between parameters obtained using the best nonlinear model (Gompertz) showed that the correlation between parameter and b was positive (72%) and the correlation between and k was negative (86%). Also correlation between parameters b and k was negative (95%). The value of coefficient of explanation and adjusted coefficient of explanation for different functions varied between 0.92 to 0.93. In general, the results showed that the Gompertz model was the best function describing the growth curve in Mazandaran native chickens that could be used in herd management to predict the future weight of birds.

Selection to increase the frequency of new mutations useful only in some subpopulations leaves markers at the genome level. Most of these regions are related to genes and QTLs associated with significant economic traits. The aim of this study was to identify selection traits related to functional traits in the number of world's sheep using modern methods. For this purpose, genotyping files were used using genomic chips related to 1591 sheep from different breeds of the world, (Dryad ،Frontiresin ،Zenodo ،Animal Genom، Hapmap( including 52 breeds from 20 countries on all continents, and 13 studies were stored in various genomic databases. First, to determine the population mixing between different breeds from regions with different climatic conditions, population structure analysis or population stratification was carried out. Fst, hapflk, iHs, Rsb, XpEHH tests were used to identify the selection signatures, and finally, a meta-analysis of the above methods was used. The population was divided into three groups by PCA analysis: AIT: Asian, Turkish and African breeds, AUNZ: Australian and New Zealand breeds, and EU: European breeds. Based on the meta-analysis test, 10 genus regions were identified on chromosomes 2, 3, 4, 5, 6, 7, 9, 12, 20, 22. For example, the MC1R gene is associated with skin color, the DSG2 gene with the quality and quantity of wool, and the AICDA gene with the production of immunoglobulin. Bioinformatics analysis showed that some of these gene regions are directly and indirectly associated with genes affecting environmental adaptation (ADAMTS9), reproduction (GNAQ), melanocyte regulation (KITLG), and immunoglobulin production (BATF, TNFSF13, AICDA, MZB1), Cell differentiation (TNFSF13, TGFB1), Immune process (AGBL5, ATG7, PRDX1, CCL26, IL17RC, TNFSF12, TGFB2, PTGS1), Cellular response to external stimulation, NKK1, PRF3 (PR) Interferon alpha (DDX58), defense response process (CCL24, FAM13B, LDLR, CLDN7), cellular homeostasis (BAMBI, DVL2), regulation of mitogen-activated protein kinase (PLCE1) and regulation of stress response (FXR2, HSPH1). Comparative analysis of meta-analysis of selection cues based on bioinformatics databases can identify selected genomic regions for traits of economic and biological importance in sheep.

 Chicken major histocompatibility complex (MHC) region are important in immune responses, resistance to diseases, and relationships with evolution processes. The chicken major histocompatibility complex is composed of two gene regions: the B and Y (Rfp-Y) loci, both located on micro chromosome 16. The B locus includes three gene classes, I (B-F), II (B-L) and IV (B-G). The chicken major histocompatibility complex, consists of several clusters of highly polymorphic genes, some of which are associated with disease resistance. The class I and class II antigens resemble their mammalian counterparts in the encoded protein structure. The class IV region encodes the B blood group antigens, which are readily identified by serological blood-typing. The class III region appears to be divided in chickens, with some elements that are MHC-linked and others that map elsewhere. In addition the Rfp-Y system, which bears a strong similarity to the MHC, maps to the opposite side of the nucleolar organizer region on the same micro chromosome as the MHC. Each class of MHC genes is a potential candidate for a role in disease resistance. The MHC genes show associations with response to diseases as diverse as virally induced neoplasia, bacterial, parasitic and auto-immune diseases.

 In this study, allelic polymorphism in B-L, B-F and B-G loci involved in the immune system in four Iranian indigenous chickens were examined using PCR-RFLP technique. Two hundred birds including common, West Azerbaijan, Marandi, Mazandarani indigenous chicken breeds were selected. As much as 1-2 ml of blood was taken from each of the chicken. Blood samples were transferred to the anticoagulant (Ethylene diamine tetra acetic acid) tubes in the vicinity of the ice to the genetic and biotechnological laboratory of Islamic Azad University, Maragheh branch and until the onset of genomic DNA extraction and subsequent experiments were kept at -20°C. In the extraction of the genomic DNA of blood samples, the salting out method and for amplify of each locus, a pair of specific primers was used. For detection of mutation in the loci the Msp I enzyme was used. For genetic analysis of data derived from digestive enzymes in indigenous chickens, POPGENE software version 1.32 was used. This software is used to estimate the allele and genotypic frequencies, observed and expected heterozygosity, mean heterozygosity, Hardy-Weinberg equilibrium, Fixation index, Shannon information index, and other genetic parameters.

 According to this study results, in the 374-bp locus of B-L, after enzymatic digestion, only BB genotype and monomorphic was detected. In the 1048 bp locus of B-F, two genotypes CG and GG were identified and the C allele included 515, 410, 75 and 47 bp bands, and the G allele also included bands of 410, 302, 213, 75 and 47 bp and the χ2 calculated in this locus was not significant for all populations (P < 0.05), and all populations were in Hardy-Weinberg equilibrium. Three Genotype MM, MN and NN genotypes were identified for the locus of B-G (401 bp), M allele included a 401 bp band and N allele included bands of 350 and 51 bp. The χ2 calculated in this locus was not significant for the indigenous chicken population of Mazandarani (P < 0.05) and this population was in Hardy-Weinberg equilibrium. The Shannon information index was calculated to be 0.37 and 0.59 in markers loci of B-F and B-G, respectively, and the fixation index values were -0.13 and -0.17, respectively. The highest observed heterozygosity index for B-L and B-G loci was 0.24 and 0.57, respectively. Estimation of the negative fixation index values in the studied chickens populations could be due to the high selection rate in the populations. The fixation index values is always variable in range -1 to 1, and its negativity indicates a decrease in heterozygosity and increase in homozygosity or increased inbreeding, as well as a deviation from the Hardy-Weinberg equilibrium in the populations. Shannon's information index is an estimate of genetic diversity in populations. In all of the populations studied, the B-G locus has a relatively high genetic diversity.

 Regarding the polymorphism in the two gene sites (B-F and B-G) studied and the heterozygosity reduction in the populations studied, can be prevented from occurrence of non-random crosses in populations and prevented the reduction of heterozygosity and thus reduced genetic diversity. Also, by studying the immune responses associated with these two gene sites, from these genes can be used as marker for genetic breeding in indigenous chickens for increase of resistance to diseases.

The aim of this study was to compare different methods of Bayesian (parameteric) approaches for predicting genomic breeding values of traits with different genetic architecture in different distribution of gene effects, number of quantitative traits loci, heritability and the number of reference population using simulated data. A genome contained 3 chromosomes, with the length of 100 cM and 1000 evenly spaced single nucleotide polymorphisms (SNP) on each chromosome was simulated. For hypotheses simulation attributes, different distribution of gene effects (uniform, normal and gamma), 3 levels of QTL (50, 200 and 400), 2 levels of heritability (0.16 and 0.5) and 2 levels of the reference population (1000 and 2000) was considered. In order to predict breeding values of individuals in the population of reference and verification, 5 Bayesian methods including Bayes Ridge regression (BRR), A, LASSO (L), Cπ and B were used. As the distance between reference population and selection candidates increased, due to disruption of linkage phase, the accuracy of genomic breeding values in all methods decreased. As heritability increased from 0.16 to 0.5, all methods showed an increase of about 0.2 in accuracy of genomic breeding values. When the number of reference population had increased from 1000 to 2000, all methods showed increased accuracy by nearly 0.18. When the distribution of gene effects was gamma, the Bayes A method showed a clear preference along with both levels of heritability compared with other methods. When the distribution of gene effects was uniform, all methods showed similar accuracy. For high heritability traits, the accuracy of prediction reduced as the number of QTL increased from 50 to 200. Conversly, in low heritability traits, there was no visible influence in accuracy of genomic breeding values.

This research used the test day data of the milk production of Holstein cows from Golestan, Mazandaran and Guilan provinces collected by Animal Breeding Center of Iran between the years of 1988-2011. Permanent effects of model, including cattle blend, breeding year and season, also percent of Holstein gene and age during recording, were used as a secondary variable. All data obtained in this research was 92943 test day records of milk production from 12855 Holstein heifers. DFREML was applied to estimate variance components and genetic parameters in test day models (with random regression restricted maximum likelihood method). A third-order Legendre polynomial has been used in order to considering genetic and permanent environmental variations of milk production for cows lactation period. Analyzing the environmental factors made by SAS software program. Maximum estimation of genetic, phenotypic and permanent environmental correlations of milk yield was 0.99, 0.61 and 0.98, respectively The results of this research shows that the heritability of milk production are different from each other in the different months of lactation period and was achieved in the range of 0.08- 0.19, respectively.