Journal of Animal Production, Print ISSN: 2008-6776., Online ISSN: 2382-994X

Comparing the performance of principal component analysisand Artificial Neural Network methods in identifying the discriminating SNP(s) in different horse breeds of the world

Document Type : Research Paper

Authors

Siavash Manzoori
Amir Hossein Khaltabadi Farahani
Mohammad Hossein Moradi
Mehdi Kazemi bon-Chenari

Department of Animal Science, Faculty of Agriculture and Natural Resources, Arak University, Arak, 38156-8-8349, Iran.

https://doi.org/10.22059/jap.2022.335575.623663
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 (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.
 

Keywords

Artificial neural network
Assignment analysis
Genetic structure
horse breeds
Principal Component Analysis
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Journal of Animal Production, Print ISSN: 2008-6776., Online ISSN: 2382-994X
Volume 24, Issue 3
Autumn 2022
Pages 259-270

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