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Ancestry informative markers derived from discriminant analysis of principal components provide important insights into the composition of crossbred cattle

机译：来自主成分判别分析的祖先的信息标记提供了对杂交牛的组成的重要见解

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The cost of SNP genotyping to screen different breeds and to estimate the exact proportion of ancestry level is quite high, which can be compensated through deriving a small panel of ancestry informative markers (AIMs). Hence, we carried out the present study to provide an insight into ancestry level inferred from a panel of informative markers in the crossbred Vrindavani population developed at ICAR-IVRI, India. We have performed a new method i.e., discriminant analysis of principal components (DAPC) for the first time on the dataset of Vrindavani cattle. To confirm our method, we had performed DAPC on two other well-known crossbred cattle, i.e., Frieswal and Beefmaster. Three sets of panels (500, 1000 and 2000 markers) were tested for clustering of individuals. Among all the panels, we found the panel (1000 markers) with DAPC based contribution method was of the smallest size and comparatively of the highest accuracy.
机译：SNP基因分型以筛选不同品种和估计祖先水平的确切比例的成本非常高，这可以通过导出小组祖先的信息标记（AIMS）来补偿。因此，我们进行了本研究，向印度Icar-Ivri的杂交Vrindavani人口中的信息型群体推断出来的祖先层次。我们首次执行了一种新的方法，即，首次在Vrindavani牛的数据集上判断了主成分（DAPC）。为了确认我们的方法，我们在另外两个众所周知的杂交牛，即Frieswal和Beefmaster上进行了DAPC。测试了三组面板（500,000和2000个标记）以进行个体的聚类。在所有面板中，我们发现具有DAPC的贡献方法的面板（1000个标记）最小，比较最高的精度。

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