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 F_ST 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 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.

中文翻译：

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全基因组选择判别性SNP标记用于本地绵羊品种的品种分配

将个体分配给真正的起源种群是基因组数据在动物育种中实际应用的最重要应用之一。这项研究的目的是开发一种统计方法，然后从高通量基因分型数据中识别出最少数量的信息性SNP标记，从而能够追踪土著绵羊品种中未知样品的真实品种。使用Illumina OvineSNP50K BeadChip在Zel，Lori-Bakhtiari，Afshari，Moqani，Qezel和野生型伊朗绵羊品种中对217只动物进行了基因分型。经过SNP质量检查后，使用主成分分析（PCA）来确定动物如何使用所有基因型标记物分配到各组。结果表明，第一个主成分（PC ₁）分离出两个家养和野生绵羊品种，并且将PC _2的所有家养品种彼此分离。使用F _ST计算不同品种之间的遗传距离和雷诺法，结果表明该品种已很好地分化。使用逐步判别分析（SDA）和线性判别分析（LDA）开发了一种统计方法，以减少用于区分6种不同伊朗绵羊种群的SNP数量，并采用K折交叉验证技术来评估分配成功率中选定的SNP子集。该程序选择了减少的标记库，分为201个SNP，它们能够以100％的准确度准确区分所有绵羊种群。此外，使用201个线性独立的SNP进行的主要成分（DAPC）的区分分析显示，这些标记能够将所有个体分配为真正的品种。最后，这201个已鉴定的SNP已成功用于包含96头Bal路支绵羊品种的独立外群品种中，结果表明这些标记物能够将所有未知样品正确分配给真正的起源种群。总的来说，这项研究的结果表明，SDA和LDA技术的组合使用代表了一种有效的策略，可用于选择减少的高判别标记物库。