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Mahdi Khodaei Motlagh

Mahdi Khodaei Motlagh

Academic rank: Professor
ORCID: https://orcid.org/0000-0002-1281-7152
Education: PhD.
ScopusId: 41861677000
Faculty: Agriculture and Environment
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Research

Title
Genome-wide selection of discriminant snp markers for breed assignment in indigenous sheep breeds
Type
JournalPaper
Keywords
Indigenous sheep breeds; Assignment methods; Discriminant SNP markers; Principal component analysis (PCA); Linear discriminant analysis (LDA)
Year
2021
Journal Annals of Animal Science
DOI
Researchers Mohammad Hossein Moradi ، Amirhossein Khaltabadi Farahani ، Mahdi Khodaei Motlagh ، Mehdi Kazemi bonchenari ، John Mak Ivan

Abstract

The assignment of an individual to the true population of origin is one of the most important applications of genomic data for practical use in animal breeding. The aim of this study was to develop a statistical method and then, to identify the minimum number of informative SNP markers from high-throughput genotyping data that would be able to trace the true breed of unknown samples in indigenous sheep breeds. The total numbers of 217 animals were genotyped using Illumina OvineSNP50K BeadChip in Zel, Lori-Bakhtiari, Afshari, Moqani, Qezel and a wild-type Iranian sheep breed. After SNP quality check, the principal component analysis (PCA) was used to determine how the animals allocated to the groups using all genotyped markers. The results revealed that the first principal component (PC1) separated out the two domestic and wild sheep breeds, and all domestic breeds were separated from each other for PC2. The genetic distance between different breeds was calculated using FST and Reynold methods and the results showed that the breeds were well differentiated. A statistical method was developed using the stepwise discriminant analysis (SDA) and the linear discriminant analysis (LDA) to reduce the number of SNPs for discriminating 6 different Iranian sheep populations and K-fold cross-validation technique was employed to evaluate the potential of a selected subset of SNPs in assignment success rate. The procedure selected reduced pools of markers into 201 SNPs that were able to exactly discriminate all sheep populations with 100% accuracy. Moreover, a discriminate analysis of principal components (DAPC) developed using 201 linearly independent SNPs revealed that these markers were able to assign all individuals into true breed. Finally, these 201 identified SNPs were successfully used in an independent out-group breed consisting 96 samples of Baluchi sheep breed and the results indicated that these markers are able to correctly allocate all unknown samples to true popula