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Standard statistical tools for the breed allocation problem

机译:品种分配问题的标准统计工具

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Modern technologies are frequently used in order to deal with new genomic problems. For instance, the STRUCTURE software is usually employed for breed assignment based on genetic information. However, standard statistical techniques offer a number of valuable tools which can be successfully used for dealing with most problems. In this paper, we investigated the capability of microsatellite markers for individual identification and their potential use for breed assignment of individuals in seventy Lidia breed lines and breeders. Traditional binomial logistic regression is applied to each line and used to assign one individual to a particular line. In addition, the area under receiver operating curve (AUC) criterion is used to measure the capability of the microsatellite-based models to separate the groups. This method allows us to identify which microsatellite loci are related to each line. Overall, only one subject was misclassified or a 99.94% correct allocation. The minimum observed AUC was 0.986 with an average of 0.997. These results suggest that our method is competitive for animal allocation and has some interpretative advantages and a strong relationship with methods based on SNPs and related techniques.
机译:为了解决新的基因组问题,经常使用现代技术。例如,通常将STRUCTURE软件用于基于遗传信息的品种分配。但是,标准的统计技术提供了许多有价值的工具,可以成功地用于处理大多数问题。在本文中,我们研究了微卫星标记用于个体识别的能力,以及它们在70个Lidia育种系和育种者中用于个体品种分配的潜在用途。传统的二项式逻辑回归适用于每条线,并用于将一个人分配给特定的线。此外,接收器工作曲线(AUC)准则下的面积用于测量基于微卫星的模型分离各组的能力。这种方法使我们能够确定哪些微卫星基因座与每条线相关。总体而言,只有一个主题被错误分类或正确分配率为99.94%。观察到的最小AUC为0.986,平均为0.997。这些结果表明,我们的方法在动物分配方面具有竞争性,并且具有一些解释性优势,并且与基于SNP和相关技术的方法有很强的联系。

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