superparamagnetic clustering

Conservation of the genetic diversity of indigenous animals is very important. For the sustainable use of genetic resources, it is necessary to first study the genetic structure of populations. The main goals of this research were to identify the population structure of Turkmen and Darehshori horses using dense SNP markers and to compare the effectiveness of PCA, DAPC, and SPC methods in clustering these populations.

For this purpose, 67 Turkmen and 39 Darehshori horses were genotyped using Illumina EquineSNP70 BeadChip. After applying quality control steps, five Turkmen horses and one Darehshori horse were removed. Then, the structure of populations was identified by three methods of principal component analysis (PCA), discriminant analysis of principal components (DAPC), and superparamagnetic clustering (SPC). These methods do not depend on previous assumptions and make it possible to analyze very large genome databases without prior knowledge of individual ancestry. These methods are also very fast and efficient.

This study compared the efficiency of these three clustering methods in identifying population structures. All three methods were successful in separating the two breeds, and Turkmen and Darehshori breeds were grouped into separate genetic groups. The difference is that the DAPC method only separated the two main populations, but the PCA and SPC methods could identify several subpopulations in each breed. The results of this study showed that the SPC method for studying the population structure of indigenous breeds with unknown information can be more useful than other methods. Therefore, using this method, a suitable program can be designed to conserve and use genetic resources.

PCA, DAPC, and SPC methods were able to successfully identify the genetic structure of Turkmen and Darehshori breeds, and in general, it can be said that the information obtained from dense SNP markers can be a powerful tool for identifying the population structure of indigenous breeds.