Empirical evaluation of genetic clustering methods using multilocus genotypes from 20 chicken breeds.
Empirical evaluation of genetic clustering methods using multilocus genotypes from 20 chicken breeds.
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发表时间：
2001-10
期刊：
影响因子：
3.3
通讯作者：
N. Rosenberg;T. Burke;K. Elo;M. Feldman;P. Freidlin;M. Groenen;J. Hillel;A. Mäki-Tanila;Michèle Tixier-Boichard;A. Vignal;K. Wimmers;S. Weigend
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作者：
N. Rosenberg;T. Burke;K. Elo;M. Feldman;P. Freidlin;M. Groenen;J. Hillel;A. Mäki-Tanila;Michèle Tixier-Boichard;A. Vignal;K. Wimmers;S. Weigend
我们测试了遗传聚类分析在确定人口结构的一个大的数据集，人口结构是以前已知的效用。每个600个人代表20个不同的鸡品种的27个微卫星位点的基因型，和个人的多位点基因型被用来推断遗传簇。每个品种的个体被推断为大多属于同一个集群。聚类成功率，衡量的比例，适当推断属于其正确的品种的个人，始终约为98%。当使用最高预期杂合性的标记时，包括来自基因分型的27个标记中的至少8-10个高度可变标记的基因型也实现>95%的聚类成功。当使用12-15个高度可变的标记并且每个品种30个个体中仅使用15-20个时，聚类成功率至少为90%。我们建议，在物种的人口结构是感兴趣的，数据库的多位点基因型在高度可变的标记应编译。然后，这些基因型可以用作遗传聚类分析的训练样本，并有助于将来源不明的个体分配到种群中。聚类算法具有潜在的应用在定义物种内的遗传单位，是有用的保护问题。
We tested the utility of genetic cluster analysis in ascertaining population structure of a large data set for which population structure was previously known. Each of 600 individuals representing 20 distinct chicken breeds was genotyped for 27 microsatellite loci, and individual multilocus genotypes were used to infer genetic clusters. Individuals from each breed were inferred to belong mostly to the same cluster. The clustering success rate, measuring the fraction of individuals that were properly inferred to belong to their correct breeds, was consistently approximately 98%. When markers of highest expected heterozygosity were used, genotypes that included at least 8-10 highly variable markers from among the 27 markers genotyped also achieved >95% clustering success. When 12-15 highly variable markers and only 15-20 of the 30 individuals per breed were used, clustering success was at least 90%. We suggest that in species for which population structure is of interest, databases of multilocus genotypes at highly variable markers should be compiled. These genotypes could then be used as training samples for genetic cluster analysis and to facilitate assignments of individuals of unknown origin to populations. The clustering algorithm has potential applications in defining the within-species genetic units that are useful in problems of conservation.