Freeman A R, Bradley D G, Nagda S, Gibson J P, Hanotte O
Department of Genetics, Smurfit Institute of Genetics, Trinity College, Dublin 2, Ireland.
Anim Genet. 2006 Feb;37(1):1-9. doi: 10.1111/j.1365-2052.2005.01363.x.
Microsatellite markers are commonly used for population genetic analyses of livestock. However, up to now, combinations of microsatellite data sets or comparison of population genetic parameters from different studies and breeds has proven difficult. Often different genotyping methods have been employed, preventing standardization of microsatellite allele calling. In other cases different sets of markers have been genotyped, providing differing estimates of population genetic parameters. Here, we address these issues and illustrate a general two-step regression approach in cattle using three different sets of microsatellite data, to combine population genetics estimates of diversity and admixture. This regression-based method is independent of the loci genotyped but requires common breeds in the data sets. We show that combining microsatellite data sets can provide new insights on the origin and geographical distribution of genetic diversity and admixture in cattle, which will facilitate global management of this livestock species.
微卫星标记常用于家畜的群体遗传学分析。然而，到目前为止，事实证明，将微卫星数据集进行组合或比较来自不同研究和品种的群体遗传参数是困难的。通常采用了不同的基因分型方法，这妨碍了微卫星等位基因分型的标准化。在其他情况下，对不同的标记集进行了基因分型，从而对群体遗传参数给出了不同的估计。在此，我们解决这些问题，并在牛中使用三组不同的微卫星数据说明了一种通用的两步回归方法，以结合群体遗传学对多样性和混合程度的估计。这种基于回归的方法与所基因分型的位点无关，但要求数据集中有共同的品种。我们表明，组合微卫星数据集可以为牛的遗传多样性和混合程度的起源及地理分布提供新的见解，这将有助于对这种家畜物种进行全球管理。
