Measuring population differentiation using GST or D? A simulation study with microsatellite DNA markers under a finite island model and nonequilibrium conditions
Measuring population differentiation using GST or D? A simulation study with microsatellite DNA markers under a finite island model and nonequilibrium conditions
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DOI：
10.1111/j.1365-294x.2011.05108.x
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发表时间：
2011-06-01
影响因子：
4.9
通讯作者：
Zhang, De-Xing
中科院分区：
文献类型：
--
作者：
Leng, Liang;Zhang, De-Xing
居群的遗传分化是居群遗传学研究中的一个重要参数。Wright的F-ST（以及它的亲属，如G（ST））一直是分化的标准度量。然而，近年来，人们越来越认识到这些指标的不足，从而提出了一些新的指标，例如Jost's D（Molecular Ecology，2008; 17，4015）。这些新的衡量标准的存在引起了相当大的辩论，并引起了一些混乱的统计数据应用于估计人口分化。在这里，我们报告了一个模拟研究与中性微卫星DNA位点下的有限岛模型G（ST）和D的性能进行比较，特别是在非平衡条件下。我们的研究结果表明，这两个统计量之间存在根本性的差异，G（ST）和D都不能令人满意地在所有情况下量化分化。D对突变模型非常敏感，但G（ST）明显不那么敏感，这限制了D在群体参数估计和遗传标记比较中的效用。起始群体的初始杂合度对G（ST）和D的个体行为及分化早期的相关行为都有重要影响，且对D的影响远大于G（ST）。在分化的早期阶段，当初始杂合性相对较低（< 0.5，如果亚群的数量很大）时，G（ST）比D增加得更快;当初始杂合性较高时，情况正好相反。因此，祖先种群的状态似乎对种群分化有一些持久的影响。一般来说，当杂合性较低时，G（ST）可以很好地测量分化，无论原因如何;然而，当杂合性较高（例如，由于高突变率或高初始杂合性）和基因流中等至强时，G（ST）无法测量分化。有趣的是，当种群规模不是很小（例如N >= 1000）时，G（ST）在基因流不存在或非常弱的情况下，即使突变率不低（例如mu = 0.001），也可以在很长一段时间内随时间线性地测量分化。相比之下，D，作为一个区分措施，在所有这些情况下，表现得相当稳健。在实践中，这两个指数都应该计算，并考虑杂合性（特别是H-S）和基因流的相对水平。我们认为，这两个指数的比较可以产生有用的见解影响人口分化的进化过程。
The genetic differentiation of populations is a key parameter in population genetic investigations. Wright's F-ST (and its relatives such as G(ST)) has been a standard measure of differentiation. However, the deficiencies of these indexes have been increasingly realized in recent years, leading to some new measures being proposed, such as Jost's D (Molecular Ecology, 2008; 17, 4015). The existence of these new metrics has stimulated considerable debate and induced some confusion on which statistics should be used for estimating population differentiation. Here, we report a simulation study with neutral microsatellite DNA loci under a finite island model to compare the performance of G(ST) and D, particularly under nonequilibrium conditions. Our results suggest that there exist fundamental differences between the two statistics, and neither G(ST) nor D operates satisfactorily in all situations for quantifying differentiation. D is very sensitive to mutation models but G(ST) noticeably less so, which limits D's utility in population parameter estimation and comparisons across genetic markers. Also, the initial heterozygosity of the starting populations has some important effects on both the individual behaviours of G(ST) and D and their relative behaviours in early differentiation, and this effect is much greater for D than G(ST). In the early stages of differentiation, when initial heterozygosity is relatively low (< 0.5, if the number of subpopulations is large), G(ST) increases faster than D; the opposite is true when initial heterozygosity is high. Therefore, the state of the ancestral population appears to have some lasting impacts on population differentiation. In general, G(ST) can measure differentiation fairly well when heterozygosity is low whatever the causes; however, when heterozygosity is high (e.g. as a result of either high mutation rate or high initial heterozygosity) and gene flow is moderate to strong, G(ST) fails to measure differentiation. Interestingly, when population size is not very small (e.g. N >= 1000), G(ST) measures differentiation quite linearly with time over a long duration when gene flow is absent or very weak even if mutation rate is not low (e.g. mu = 0.001). In contrast, D, as a differentiation measure, performs rather robustly in all these situations. In practice, both indexes should be calculated and the relative levels of heterozygosities (especially H-S) and gene flow taken into account. We suggest that a comparison of the two indexes can generate useful insights into the evolutionary processes that influence population differentiation.