Álvarez Isabel, Fernández Iván, Traoré Amadou, Menéndez-Arias Nuria A, Goyache Félix
Servicio Regional de Investigación y Desarrollo Agroalimentario, E-33394 Gijón, Spain.
Institut de l'Environnement et des Recherches Agricoles (INERA), Ouagadougou 04 BP 8645, Burkina Faso.
Animals (Basel). 2021 Jan 11;11(1):151. doi: 10.3390/ani11010151.
A sample of 185 West African cattle belonging to nine different taurine, sanga, and zebu populations was typed using a set of 33 microsatellites and the BovineHD BeadChip of Illumina. The information provided by each type of marker was summarized via clustering methods and principal component analyses (PCA). The aim was to assess differences in performance between both marker types for the identification of population structure and the projection of genetic variability on geographical maps. In general, both microsatellites and Single Nucleotide Polymorphism (SNP) allowed us to differentiate taurine cattle from zebu and sanga cattle, which, in turn, would form a single population. Pearson and Spearman correlation coefficients computed among the admixture coefficients (fitting K = 2) and the eigenvectors corresponding to the first two factors identified using PCA on both microsatellite and SNP data were statistically significant (most of them having < 0.0001) and high. However, SNP data allowed for a better fine-scale identification of population structure within taurine cattle: Lagunaire cattle from Benin were separated from two different N'Dama cattle samples. Furthermore, when clustering analyses assumed the existence of two parental populations only (K = 2), the SNPs could differentiate a different genetic background in Lagunaire and N'Dama cattle. Although the two N'Dama cattle populations had very different breeding histories, the microsatellite set could not separate the two N'Dama cattle populations. Classic bidimensional dispersion plots constructed using factors identified via PCA gave different shapes for microsatellites and SNPs: plots constructed using microsatellite polymorphism would suggest the existence of weakly differentiated, highly intermingled, subpopulations. However, the projection of the factors identified on synthetic maps gave comparable images. This would suggest that results on population structuring must be interpreted with caution. The geographic projection of genetic variation on synthetic maps avoids interpretations that go beyond the results obtained, particularly when previous information on the analyzed populations is scant. Factors influencing the performance of the projection of genetic parameters on geographic maps, together with restrictions that may affect the election of a given type of markers, are discussed.
使用一组33个微卫星和Illumina公司的牛HD基因分型芯片，对来自9个不同的瘤牛、桑加牛和泽布牛种群的185头西非牛样本进行了基因分型。通过聚类方法和主成分分析（PCA）总结了每种标记类型提供的信息。目的是评估两种标记类型在识别种群结构和在地理地图上投影遗传变异方面的性能差异。总体而言，微卫星和单核苷酸多态性（SNP）都使我们能够区分瘤牛与泽布牛和桑加牛，而泽布牛和桑加牛又会形成一个单一的种群。在微卫星和SNP数据上使用PCA确定的混合系数（拟合K = 2）与对应于前两个因子的特征向量之间计算的皮尔逊和斯皮尔曼相关系数具有统计学意义（大多数<0.0001）且很高。然而，SNP数据能够更好地在瘤牛中进行种群结构的精细识别：来自贝宁的拉古奈牛与两个不同的恩达马牛样本分开。此外，当聚类分析仅假设存在两个亲本种群（K = 2）时，SNP能够区分拉古奈牛和恩达马牛的不同遗传背景。尽管两个恩达马牛种群的繁殖历史非常不同，但微卫星组无法区分这两个恩达马牛种群。使用通过PCA确定的因子构建的经典二维散点图对于微卫星和SNP给出了不同的形状：使用微卫星多态性构建的图表明存在弱分化、高度混合的亚种群。然而，在合成地图上确定的因子投影给出了可比的图像。这表明关于种群结构的结果必须谨慎解释。遗传变异在合成地图上的地理投影避免了超出所获得结果的解释，特别是当关于分析种群的先前信息很少时。讨论了影响遗传参数在地理地图上投影性能的因素，以及可能影响给定类型标记选择的限制。
