<abstract xmlns="http://www.w3.org/1999/xhtml">

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 of 96 samples of Baluchi sheep breed and the results indicated that these markers are able to correctly allocate all unknown samples to true population of origin. In general, the results of this study indicated that the combined use of the SDA and LDA techniques represents an efficient strategy for selecting a reduced pool of highly discriminant markers.
</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 of 96 samples of Baluchi sheep breed and the results indicated that these markers are able to correctly allocate all unknown samples to true population of origin. In general, the results of this study indicated that the combined use of the SDA and LDA techniques represents an efficient strategy for selecting a reduced pool of highly discriminant markers.

Genome-wide selection of discriminant SNP markers for breed assignment in indigenous sheep breeds

Department of Animal Science, Faculty of Agriculture and Natural Resources

Annals of Animal Science

This work is licensed under the Creative Commons Attribution 4.0 International License.

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...

Genome-wide selection of discriminant SNP markers for breed assignment in indigenous sheep breeds

Published Online: Aug 05, 2021

Page range: 807 - 831

Received: Feb 28, 2020

Accepted: Sep 09, 2020

DOI: https://doi.org/10.2478/aoas-2020-0097

Keywords
indigenous sheep breeds, assignment methods, discriminant SNP markers, principal component analysis (PCA), linear discriminant analysis (LDA)

© 2021 Mohammad Hossein Moradi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Genome-wide selection of discriminant SNP markers for breed assignment in indigenous sheep breeds

Published Online: Aug 05, 2021

Page range: 807 - 831

Received: Feb 28, 2020

Accepted: Sep 09, 2020

DOI: https://doi.org/10.2478/aoas-2020-0097

Keywordsindigenous sheep breeds, assignment methods, discriminant SNP markers, principal component analysis (PCA), linear discriminant analysis (LDA)

© 2021 Mohammad Hossein Moradi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
indigenous sheep breeds, assignment methods, discriminant SNP markers, principal component analysis (PCA), linear discriminant analysis (LDA)