Siavash Manzoori; Amir Hossein Khaltabadi Farahani; Mohammad Hossein Moradi
Volume 25, Issue 1 , April 2023, , Pages 1-11
Abstract
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 ...
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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.
Siavash Manzoori; Amir Hossein Khaltabadi Farahani; Mohammad Hossein Moradi; Mehdi Kazemi bon-Chenari
Volume 24, Issue 3 , October 2022, , Pages 259-270
Abstract
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 ...
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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.
Mohammad Abdoli; Mohammad Bagher Zandi; Taher harkinezhad; Masoud Kahlili
Volume 23, Issue 2 , July 2021, , Pages 155-163
Abstract
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 ...
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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.