Articles | Volume 59, issue 4
https://doi.org/10.5194/aab-59-477-2016
https://doi.org/10.5194/aab-59-477-2016
Original study
 | 
08 Dec 2016
Original study |  | 08 Dec 2016

Genetic diversity and distance of Iranian goat breeds (Markhoz, Mahabadi and Lori) compared to the Beetal breed using inter-simple sequence repeat (ISSR) markers

Leila Simaei-Soltani, Alireza Abdolmohammadi, Alireza Zebarjadi, and Saheb Foroutanifar

Abstract. The aim of this study was to investigate the genetic diversity and structure in three Iranian native goat breeds (Markhoz, Mahabadi and Lori) and the Beetal imported breed using inter-simple sequence repeat (ISSR) markers and also to investigate ISSR markers' potential in order to genetically separate single (S) and twin-birth (T) subpopulations. Blood samples were collected from 210 animals for this purpose. In total, 16 primers were used, and finally 5 primers were selected based on the number of clear bands and the level of polymorphisms. The result of this study showed that 76 of 86 observed fragments were polymorphic. Genetic diversity for each breed ranged from 0.23 in the Beetal breed to 0.26 in the Markhoz breed; this represents a relatively similar genetic diversity in these breeds. An unweighted pair group method with arithmetic mean (UPGMA) dendrogram based on the Nei's standard genetic distance between the breeds studied showed that three Iranian goat breeds (Mahabadi, Lori and Markhoz) were clustered closer together, while the Beetal breed formed a separate cluster. In the constructed dendrogram of the subpopulations, the S and T subpopulations of each breed were clustered together. The constructed dendrogram of the Beetal breed and the S and T subpopulations of all breeds studied showed a separate cluster for the Beetal breed as an imported breed and another cluster for the S and T subpopulations as Iranian native breeds. The current study showed that the ISSR markers studied had no potential to genetically separate S and T subpopulations. On the other hand, these ISSR markers can be used for the clustering of distinct populations.