m. r. mohammadabadi

Milk has long been considered as a complete food supply and supplying part of the daily nutritional needs of humans. Kappa casein has the most important role in stability of casein micelles, size and their specific function among the milk proteins. Also it has an important role in the quality of the produced cheese and speed cottage and the process of changing milk to cheese. Hence, the purpose of this study was to investigate polymorphism in the exon 4 gene of CSN3 using the PCR-RFLP technique. For this purpose, blood samples were taken from 75 goats (Sannan, Raini and wild breeds), and genomic DNA was extracted. The polymerase chain reaction (PCR) was performed for amplification of exon 4 on CSN3 gene with a length of 645bp using a pair of specific primer. PCR productions were separated using agarose gel and it was found that the intended fragment was amplified well. The amplified fragment was digested with HaeIII enzyme and determining the genotype was performed on the 2% agarose. The result of studies carried out on the goat breeds indicated that the population was monomorphic for the kappa casein gene. Lack of the polymorphism, can be because of no mating with others goat breeds, the closed breeding place and small sample size probably, therefore, next studies on more number of these animals should aim to determine crucial relationship between kappa casein genotypes and maternal characteristics, as selection could be enhanced by the inclusion of genetic markers in breeding decisions.

The purpose of this study was applying artificial intelligence for estimating breeding values of growth traits in Kermani sheep. The records of 867 lambs including animal sex, maternal age, birth weight and weaning weight (3 months old) were used for this purpose. These data were firstly analyzed using ASReml software and then was pre-processed for applying by the MATLAB software. After initial experiments on appropriate architecture for neural networks, it was found that numbers of neurons for input layer were 3 neurons for birth weight and 6 neurons for 3 months weight. The used model in this study was multilayer perceptron (MLP) and applied algorithm was back-propagation to minimize the mean square error. From total data, 70% used as training, 15% for testing and 15% were considered as validation. During the training process, network learning rate was measured regularly by the objective functions and was used network having the minimum error and maximum accuracy. The MLP neural network with 3 input variables, 8 neurons in hidden layer and with correlation coefficient 0.73 has ability to predict breeding values of birth weight and also the MLP neural network with 6 input variables, 3 neurons in hidden layer and with correlation coefficient 0.74 has ability to predict breeding values of 3 months weight. It can be concluded that artificial neural network has a good ability to predict breeding values of growth traits in Kermani sheep with acceptable speed and accuracy and can be used to estimate breeding values for all traits in livestock.

Identification of associated genes with energy balance, yield and feed intake are recent interests of the animal breeding researchers. With consider to rich resources of animals, in our country-Iran, accomplish a little assay for identification of genes that controlling its traits with molecular techniques. Identify the candidate genes in sheep breeds using DNA test can have a great help for their breeding progress. In this research, for analysis of leptin gene polymorphism in Kermani sheep, we collected blood samples of one hundred and twenty male and female Kermani sheeps which were rearing in breeding center of Shahre Babak, Kerman. Genomic DNA was extracted using commercial DNA extraction Kit. The exon 3 (275 bp segment of exon 3) of leptin gene was amplified with specific primers. The single stranded conformation polymorphism (SSCP) patterns of PCR product were studied using Acrilamid gel and silver staining method. We obtained 10 band patterns for leptin gene: A/A, C/C, A/B, A/C, A/B/C, A/B/E, A/B/F, A/C/F, A/B/D/E and A/B/C/F. The results showed that leptin gene is so polymorphic in studied population.