BACKGROUND Due to the advent of SNP array technology, a genome-wide analysis of genetic differences between populations and breeds has become possible at a previously unattainable level. The Wright's fixation index (Fst) and the principal component analysis (PCA) are widely used methods in animal genetics studies. In paper we compared the power of these methods, their complementing each other and which of them is the most powerful. RESULTS Comparative analysis of the power Principal Components Analysis (PCA) and Fst were carried out to reveal genetic differences between herds of Holsteinized cows. Totally, 803 BovineSNP50 genotypes of cows from 13 herds were used in current study. Obtained Fst values were in the range of 0.002-0.012 (mean 0.0049) while for rare SNPs with MAF 0.0001-0.005 they were even smaller in the range of 0.001-0.01 (mean 0.0027). Genetic relatedness of the cows in the herds was the cause of such small Fst values. The contribution of rare alleles with MAF 0.0001-0.01 to the Fst values was much less than common alleles and this effect depends on linkage disequilibrium (LD). Despite of substantial change in the MAF spectrum and the number of SNPs we observed small effect size of LD - based pruning on Fst data. PCA analysis confirmed the mutual admixture and small genetic difference between herds. Moreover, PCA analysis of the herds based on the visualization the results of a single eigenvector cannot be used to significantly differentiate herds. Only summed eigenvectors should be used to realize full power of PCA to differentiate small between herds genetic difference. Finally, we presented evidences that the significance of Fst data far exceeds the significance of PCA data when these methods are used to reveal genetic differences between herds. CONCLUSIONS LD - based pruning had a small effect on findings of Fst and PCA analyzes. Therefore, for weakly structured populations the LD - based pruning is not effective. In addition, our results show that the significance of genetic differences between herds obtained by Fst analysis exceeds the values of PCA. Proposed, to differentiate herds or low structured populations we recommend primarily using the Fst approach and only then PCA.

中文翻译：

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评估主要成分的功效和赖特的固定指数分析可揭示荷斯坦奶牛群之间全基因组的遗传差异。

背景技术由于SNP阵列技术的出现，在种群和品种之间的遗传差异的全基因组分析已经可以在以前无法实现的水平上进行。赖特的固定指数（Fst）和主成分分析（PCA）是动物遗传学研究中广泛使用的方法。在本文中，我们比较了这些方法的威力，它们的互补性以及哪种方法最有效。结果进行了功率主成分分析（PCA）和Fst的比较分析，以揭示荷斯坦奶牛群之间的遗传差异。本研究共使用了来自13个牛群的803个BovineSNP50基因型奶牛。获得的Fst值在0.002-0.012（平均0.0049）的范围内，而对于MAF为0.0001-0.005的稀有SNP，它们的更小值在0.001-0的范围内。01（平均0.0027）。Fst值如此之小的原因是牛群中奶牛的遗传相关性。MAF 0.0001-0.01的稀有等位基因对Fst值的贡献远小于普通等位基因，这种作用取决于连锁不平衡（LD）。尽管MAF谱图和SNP的数量发生了实质性变化，我们仍观察到基于Fst数据的LD修剪的影响大小较小。PCA分析证实了牛群之间的相互混合和较小的遗传差异。此外，基于可视化单个特征向量的结果对畜群进行PCA分析不能用于显着区分畜群。仅应使用求和的特征向量来实现PCA的全部功能，以区分不同群体之间的遗传差异。最后，我们提供的证据表明，当使用这些方法揭示牛群之间的遗传差异时，Fst数据的重要性远远超过PCA数据的重要性。结论基于LD的修剪对Fst和PCA分析的发现影响很小。因此，对于结构较弱的人群，基于LD的修剪无效。此外，我们的结果表明，通过Fst分析获得的牛群之间遗传差异的重要性超过了PCA的值。建议，为了区分畜群或低结构人群，我们建议主要使用Fst方法，然后才使用PCA。对于结构较弱的人群，基于LD的修剪无效。此外，我们的结果表明，通过Fst分析获得的牛群之间遗传差异的重要性超过了PCA的值。建议，为了区分畜群或低结构人群，我们建议主要使用Fst方法，然后才使用PCA。对于结构较弱的人群，基于LD的修剪无效。此外，我们的结果表明，通过Fst分析获得的牛群之间遗传差异的重要性超过了PCA的值。建议，为了区分畜群或低结构人群，我们建议主要使用Fst方法，然后才使用PCA。