جستجوی مقالات مرتبط با کلیدواژه "genetic structure" در نشریات گروه "علوم دام"

The present study was conducted in order to select effective markers in breed discrimination and compare the performance of SNP marker selection methods with the data of 304 animals from 14 different breeds that were genotyped using the Illumina SNP50K marker panel. Knowledge of genetic structure are very important for better understanding of genetic changes in genomic studies. The information content of each marker is used as an index for selecting markers in reducing the size of marker panels. To estimate the information content of each marker, the following selection methods were used: Fst (pairwise & global), Theta, Delta, D, Gst, G'st, G"st and Principal Component Analysis. In this study, the logarithm of the likelihood ratio was used to select markers. According to the results, all selection methods for identifying markers had similar performance. The number of common markers between the methods was at least 42 markers and at most 499 SNP markers. In general, the F_ST statistical method required a smaller number of markers to achieve a successful assignment. G'st and G"st statistics showed poor performance with more than 350 markers to achieve 95% correct assignment. It should be noted that with only the top 60 selected markers, it is possible to achieve a success rate of more than 70%. According to the results, Wright's paired Fst had better performance than other SNP selection methods. The obtained results lead to the creation of exclusive panels to identify various breeds, which have great economic importance.

The aim of this research was to compare the efficiency and performance of the advanced artificial neural network method with the principal component analysis method in discriminating different horse breeds. In this study, two methods of perceptron neural network (Olden) and the principal component analysis (PCA), were used to identify a subset of SNP markers with the highest breed discrimination potential and to investigate how to assign animals to their breed groups. The results showed that the network method (Olden),  is able to separate all the 37 horse breeds with a small subset of SNP markers (8,000 markers) with a same capability to all genomic markers (98% accuracy). The PCA selection method was only able to identify and separate breeds with diverse geographical originations. According to the obtained results, the PCA method is not error-free and depends upon changes and modifications to run on genomic data. The results of this study provide practical approaches in the design of economic arrays for discriminating the different horse breeds.

The aim of this study was to investigate the genetic structure of Iranian native horse breeds to compare genetic diversity and understanding the relationships between the populations using 11 ISAG microsatellite markers. For this reason, 565 samples of the Iranian Equestrian database from different ages including the Iranian Arab Asil, Caspian, Darehshouri, Kurdish and Turkmen and 113 samples were used for each breed. The number of observed alleles for each locus was 7 to 19 alleles with an average of 10.81 alleles, and it was 19 for ASB17 and 7 for HTG4 locus with the highest and lowest observed alleles ranking respectively. The average observed heterozygosity from the largest to the lowest rank was, Turkmen (0.68±0.11), Caspian (0.67±0.07), Kurdish (0.66±0.06) Darehshouri (0.65±0.07), and Arab Asil (0.62±0.08). Population structure analysis with UPGMA method showed that Caspian and Kurdish populations were grouped as a unit cluster while the other populations grouped as a separate cluster. These results confirmed this hypothesis that the Caspian and Kurdish populations are close to the Nisa horses. In general, the results of this study indicate that the Iranian native horses have got a high genetic diversity, despite of populations have genetic similarity and the other hand genetic clustering of the populations is consistent with their geographic distances. The result of this study shows that the ISAG microsatellite markers are polymorphic and have more efficiency for assignment genetic diversity and genetic structure analysis of Iranian native horse breeds.

The objective of this study was to detect genetic structure of Iranian indigenous cattle using dense single nucleotide polymorphism (SNP) markers. In total, 90 cattle individuals comprising Sarabi (n=20), Kurdi (n=10), Mazandarani (n=10), Taleshi (n=10), Sistani (n=10), Kermani (n=10), Najdi (n=10) and Fars (n=10) breeds were sampled. Genotyping was performed using Illumina High-density Bovine BeadChip designed to genotype 777,962 SNPs. After removing sites being in linkage disequilibrium (pairwise sites having r2 greater than 0.2), 64333 SNPs remained for further analyses. STRUCTURE software was used to detect genetic structure of native cattle in this data set. Number of assumed populations per run was selected between two to eight. Results of this study indicated that in the first level of clustering (K=2), Sarabi (97.6%) and Kurdi (94.3%) populations shared the same cluster and Sistani (92%) had another separate cluster while other breeds were admixed from these two clusters. Kurdi population appeared as an independent cluster at K=3. Most suitable number of genetic groups in data set was explained by three clusters (K=3). Genetic differentiation of Iranian indigenous cattle was well detected in this study. Also, similarities of geographical distribution and production characteristics were in good accordance with founded genetic relationships in this study.

To finding out the genetic structure of Iranian native horses, as animal blood samples were obtained from diverse areas of Iran, in addition, 114 sequences of equine D-loop region were obtained from GenBank. Total DNA of the samples were extracted through salting out procedure and utilized as template for amplification and sequencing of the D-loop region of mtDNA. Sequence analysis of the 247-bp D-loop region of mtDNA in all samples revealed a total of 44 haplotypes with 39 polymorphic sites. Six haplogroups (A–F) were identified in the phylogenic tree drowned for these haplotypes and sequences from GenBank. Total nucleotide diversity was obtained with in the range of 0.028 ± 0.009 in Iranian native horses. Fixation index values, employing Kimura-2 parameter method ranged from -0.001 to 0.172. Analysis of Molecular Variance (AMOVA) identified that some of these populations, especially Sistani horses, bear a genetic similarity with other populations and also with sequences from the GenBank (P > 0.05). Arab and Turkman horse populations displayed non-significant difference (P > 0.05), which may reflect the uncontrolled crossing between these two breeds. Caspian and Kurd horse populations showed significant differences with other populations and sequences from the GenBank (P > 0.05). The results of the current study indicate a high genetic diversity and as well as genetic similarity in some populations. According to the dateained results it is necessary to control the crosses between the animals populations and the production of progenies in Iranian native horses.