Mahdi Mokhber; Mohammad Moradi Shahre Babak; Mostafa Sadeghi; Hossein Moradi Shahrbabak; Javad Rahmani-Nia
Abstract
In order to estimate the effective population size (Ne) in Iranian water buffalo blood and hair samples of 407 individual from Azari (N=260), Khuzestani (N=120) and Mazandrani (N=27) buffalo populations were gathered. After DNA extaraction, the samples were genotyped using Axiom® Buffalo Genotyping ...
Read More
In order to estimate the effective population size (Ne) in Iranian water buffalo blood and hair samples of 407 individual from Azari (N=260), Khuzestani (N=120) and Mazandrani (N=27) buffalo populations were gathered. After DNA extaraction, the samples were genotyped using Axiom® Buffalo Genotyping 90K Array. The Ne was estimated from 700 to 4 generations ago and also for the present generation by linkage disequiblirum data and based on heterozygote-excess method using NeEstimator (V2), respectively. Estimated Ne for Azari, Khuzestani and Mazandarani were calculated 1530, 1375 and 1141, respectively, for 700 generations ago. Ne for the present generation in Azeri, Khuzestani and Mazandarani were estimated 447, 226 and 35, respectively. The Ne for Azeri and Khuzestani were relatively high and these two populations were not endanger to extinction, but their Ne has been declined in the resent generations massively and it is necessary to care about the maintenance of Ne and relatively high diversity for these populations. However, the Mazandarani population is endangered because of low Ne and so it is necessary to carefully monitor their effective population size, improve the profitability of production and planning a suitable mating scheme to control inbreeding and genetically conserve this population.
Mahdi Mokhber; Mohammad Moradi Shahre Babak; Mostafa Sadeghi; Hossein Moradi Shahrbabak; Javad Rahmani-Nia
Abstract
In order to detect signature of selection on buffalo genome, a set of 287 water buffalo samples from 260 Azari and 27 Mazandarani buffalo breeds were genotyped using the Axiom® Buffalo Genotyping 90K Array. The unbiased fixation index method (FST) was used to detect signatures of selection. In total, ...
Read More
In order to detect signature of selection on buffalo genome, a set of 287 water buffalo samples from 260 Azari and 27 Mazandarani buffalo breeds were genotyped using the Axiom® Buffalo Genotyping 90K Array. The unbiased fixation index method (FST) was used to detect signatures of selection. In total, 14 regions with outlier FST values (0.1%) were identified. Annotation of these regions using the UMD3.1 Bos taurus Genome Assembly was performed to find putative candidate genes and QTLs within the selected and 105 genes and 28 QTLs with selection signatures were detected. A high proportion of identified genes (N=27) in regions under selection were involved in olfactory receptor, also some of the detected genes were associated with growth and body development, metabolicand apoptosis possesses, immune system development, and mammary gland development. Some of the identified QTLs in regions under selection were associated with growth traits such as body weight at birth, weaning and mature, subcutaneous fat, meat yield and carcass weight. The detected QTL for milk traits were only associated with milk contents and somatic cell count. However, it is recommended to carry out association studies to show the actual function of these genes.
Javad Rahmaninia; Seyed Reza Miraei-Ashtiani; Hossein Moradi Shahrbabak
Abstract
High through put sequencing of single nucleotide polymorphisms (SNP) has revolutionized the fine scale analysis of the population structure in different species. Various methods have been proposed and used for the study of population structure using whole-genome marker data that each has advantages and ...
Read More
High through put sequencing of single nucleotide polymorphisms (SNP) has revolutionized the fine scale analysis of the population structure in different species. Various methods have been proposed and used for the study of population structure using whole-genome marker data that each has advantages and disadvantages with respect to their characteristics. Super Paramagnetic Clustering (SPC) which is based on data mining was used in this study in order to investigate the population and sub-population structures in simulated populations. The purpose of applying this method was to achieve population structure without using any information from ancestral population. After editing the data, 29209 autosomal markers from 159 animals were analyzed. The results showed that animals are placed properly in their respective population and sub-populations based on their similarities and dissimilarities. The main advantages of this method are the computational efficiency and not requiring any prior assumptions. Therefore, it might be used to analyze the data from thousands of animals without any pedigree and ancestry information to reveal their population structure.
