Iranian Journal of animal Science

Iranian Journal of animal Science

Genetic classification of Azari and North ecotype Buffalo population using SVM method

Document Type : Research Paper

Authors
Zahra Azizi 1
Hossein Moradi Shahrbabak 2
Mohammad Moradi Shahrbabak 3
Abbas Rafat 2
Jalil Shodja 4
1 Ph.D. Student, Department of Animal Sciences, Faculty of Agricultural Sciences, University of Tabriz, Iran
2 Associate Professor, Department of Animal Sciences, Faculty of Agricultural Sciences, University of Tabriz, Iran
3 Professor, Department of Animal Sciences, University College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran
4 Professor, Department of Animal Sciences, Faculty of Agricultural Sciences, University of Tabriz, Iran
10.22059/ijas.2016.59033
Abstract
The purpose of this research was to classify buffaloes from different areas of the two Azari (West and East Azarbayjan and Ardabil provinces) and North (Guilan province) ecotypes using support vector machine method. A total of 258 buffalo were sampled and genotyped using the Axiom Buffalo 90K Genotyping Array at the Parco Technologic Padano lab in Italy. Two metric methods of cross validation and the area under the receiver operating characteristic (AUC) were used to determine the predictive performance of support vector machine (SVM) to classify individuals. The results of cross validation and methods for classifying different regions of the two ecotypes (4 provinces) were 92% and 96%, respectively that showed despite the difficulty of identifying individuals from provinces close to each other, support vector machine (SVM) method shows higher accuracy in assigning animals to their herds. Result of two ecotypes showed accuracy about 96% and 98% which represents the better ability to separate the two ecotypes. Machine learning method provides predictions for classification of each individual which can be efficient in quality control and genetic studies.
Keywords
Buffalo
Classification
SNPChip 90K
Support vector Machine
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Iranian Journal of animal Science
Volume 47, Issue 2 - Serial Number 2
Autumn 2016
Pages 279-290

  • Receive Date 05 January 2016
  • Revise Date 13 April 2016
  • Accept Date 13 April 2016