Department of Biology, Stanford University, Stanford, California.
Mol Ecol. 2019 Apr;28(7):1624-1636. doi: 10.1111/mec.15000.
Statistics and Jost's D have been proposed for replacing F as measures of genetic differentiation. A principal argument in favour of these statistics is the independence of their maximal values with respect to the subpopulation heterozygosity H , a property not shared by F . Nevertheless, it has been unclear if these alternative differentiation measures are constrained by other aspects of the allele frequencies. Here, for biallelic markers, we study the mathematical properties of the maximal values of and D, comparing them to those of F . We show that and D exhibit the same peculiar frequency-dependence phenomena as F , including a maximal value as a function of the frequency of the most frequent allele that lies well below one. Although the functions describing , D, and F in terms of the frequency of the most frequent allele are different, the allele frequencies that maximize them are identical. Moreover, we show using coalescent simulations that when taking into account the specific maximal values of the three statistics, their behaviours become similar across a large range of migration rates. We use our results to explain two empirical patterns: the similar values of the three statistics among North American wolves, and the low D values compared to and F in Atlantic salmon. The results suggest that the three statistics are often predictably similar, so that they can make quite similar contributions to data analysis. When they are not similar, the difference can be understood in relation to features of genetic diversity.
统计量和 Jost 的 D 已被提议用于替代 F 作为遗传分化的度量。这些统计量的一个主要优点是它们的最大值相对于亚群杂合度 H 是独立的,这是 F 不具备的特性。然而,这些替代分化度量是否受到等位基因频率的其他方面的限制还不清楚。在这里,对于二倍体标记,我们研究了和 D 的最大值的数学性质,并将它们与 F 的进行了比较。我们表明,和 D 与 F 一样表现出相同的特殊频率依赖性现象,包括作为最常见等位基因频率函数的最大值,其值远低于 1。尽管描述、D 和 F 的频率的函数不同,但使它们最大化的等位基因频率是相同的。此外,我们使用合并模拟表明,当考虑到三个统计量的特定最大值时,它们的行为在很大的迁移率范围内变得相似。我们使用我们的结果来解释两个经验模式:北美狼中三个统计量的值相似,以及大西洋鲑鱼中与和 F 相比 D 值较低。结果表明,这三个统计量通常是可预测的相似,因此它们可以对数据分析做出相当相似的贡献。当它们不相似时,可以根据遗传多样性的特征来理解差异。