Comparison of the diffrent clustering methods for population structure of Sarabi and Nadjdi cows by using dense genetic markers

Document Type : Research Paper

Authors

1 Zabol university,Agri facualty,zabol, iran

2 Department of Animal Science, Faculty of Agriculture, University of Zabol, Zabol, Iran

3 Department of Animal Science.Faculty of Agriculture, University of Zabol,Zabol,Iran

4 Department of Animal Science Faculty of Agricultural Science and Engineering College of Agriculture and Natural Resources University of Tehran

Abstract

Objective
So far, various methods have been used to investigate the structure of the population using the markers available in the whole genome (single-nucleotide polymorphism (SNP)), each of which has Weakness and strength. In the present study, an unsupervised network clustering (SPC), a data-mining method, was used to survey the population structure of the Sarabi and Najdi cows.
Materials and methods
The study population 424 cattle consisted of 213sarabi cattle and 211 Najdi cattle, sequenced with Illumina Bead Chip 40 K v 2 for single nucleotide markers. SORTING POINTS INTO NEIGHBORHOOD(SPIN) was used to analyze population structure. After editing data, 27859 autosomal markers were analyzed.
Results
Clustering results based on the similarities and differences between nucleotides led to the classification of two base populations and nine clusters.
Conclusions
Comparison the number of samples and other existing methods for population layering, the use of the SPIN method with high computational efficiency and the needn`t for prior assumptions makes it possible to analyze the structure of populations.
 
Citation: Khasaliaghtaei A, Vafaye Valleh M, Dashab GR, Moradi Shahrbabak H (2019) Comparison of the Different Clustering Methods for Population Structure of Sarabi and Nadjdi Cows by Using Dense Genetic Markers. Agricultural Biotechnology Journal 11 (1), 25-54. 
 
Agricultural Biotechnology Journal 11 (1), 25-54.
DOI: 10.22103/jab.2019.13193.1098
Received:  January 16, 2019; Accepted: April 28, 2019
© Faculty of Agriculture, Shahid Bahonar University of Kerman-Iranian Biotechnology Society

Keywords

References

منابع
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Agricultural Biotechnology Journal
Volume 11, Issue 1 - Serial Number 33
Volume 11, No 1
June 2019
Pages 25-54

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