Departamento de Ecología, Genética y Evolución, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina . ; Instituto de Ecología, Genética y Evolución de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina .
Genet Mol Biol. 2014 Mar;37(1):64-72. doi: 10.1590/s1415-47572014000100012. Epub 2013 Feb 28.
Bayesian clustering as implemented in STRUCTURE or GENELAND software is widely used to form genetic groups of populations or individuals. On the other hand, in order to satisfy the need for less computer-intensive approaches, multivariate analyses are specifically devoted to extracting information from large datasets. In this paper, we report the use of a dataset of AFLP markers belonging to 15 sampling sites of Acacia caven for studying the genetic structure and comparing the consistency of three methods: STRUCTURE, GENELAND and DAPC. Of these methods, DAPC was the fastest one and showed accuracy in inferring the K number of populations (K = 12 using the find.clusters option and K = 15 with a priori information of populations). GENELAND in turn, provides information on the area of membership probabilities for individuals or populations in the space, when coordinates are specified (K = 12). STRUCTURE also inferred the number of K populations and the membership probabilities of individuals based on ancestry, presenting the result K = 11 without prior information of populations and K = 15 using the LOCPRIOR option. Finally, in this work all three methods showed high consistency in estimating the population structure, inferring similar numbers of populations and the membership probabilities of individuals to each group, with a high correlation between each other.
贝叶斯聚类分析方法（如 STRUCTURE 或 GENELAND 软件中实现的方法）广泛用于形成群体或个体的遗传群体。另一方面，为了满足对计算资源要求较低的方法的需求，多元分析专门用于从大型数据集提取信息。在本文中，我们报告了 AFLP 标记数据集的使用情况，该数据集属于 15 个取样地点的金合欢属植物，用于研究遗传结构，并比较了三种方法的一致性：STRUCTURE、GENELAND 和 DAPC。在这些方法中，DAPC 是最快的方法，并且在推断种群数量（使用 find.clusters 选项推断 K = 12，使用种群先验信息推断 K = 15）方面具有准确性。GENELAND 反过来提供了有关个体或群体在指定坐标时（K = 12）在空间中的成员概率的信息。STRUCTURE 还根据祖先推断种群数量和个体的成员概率，在没有种群先验信息的情况下得出结果 K = 11，在使用 LOCPRIOR 选项的情况下得出结果 K = 15。最后，在这项工作中，所有三种方法都在估计种群结构方面表现出高度的一致性，推断出相似数量的种群和个体对每个群体的成员概率，彼此之间具有高度的相关性。
