Bertorelle G, Excoffier L
Department of Integrative Biology, University of California, Berkeley, USA.
Mol Biol Evol. 1998 Oct;15(10):1298-311. doi: 10.1093/oxfordjournals.molbev.a025858.
We derive here two new estimators of admixture proportions based on a coalescent approach that explicitly takes into account molecular information as well as gene frequencies. These estimators can be applied to any type of molecular data (such as DNA sequences, restriction fragment length polymorphisms [RFLPs], or microsatellite data) for which the extent of molecular diversity is related to coalescent times. Monte Carlo simulation studies are used to analyze the behavior of our estimators. We show that one of them (mY) appears suitable for estimating admixture from molecular data because of its absence of bias and relatively low variance. We then compare it to two conventional estimators that are based on gene frequencies. mY proves to be less biased than conventional estimators over a wide range of situations and especially for microsatellite data. However, its variance is larger than that of conventional estimators when parental populations are not very differentiated. The variance of mY becomes smaller than that of conventional estimators only if parental populations have been kept separated for about N generations and if the mutation rate is high. Simulations also show that several loci should always be studied to achieve a drastic reduction of variance and that, for microsatellite data, the mean square error of mY rapidly becomes smaller than that of conventional estimators if enough loci are surveyed. We apply our new estimator to the case of admixed wolflike Canid populations tested for microsatellite data.
我们基于一种明确考虑分子信息以及基因频率的合并方法，推导出了两种新的混合比例估计量。这些估计量可应用于分子多样性程度与合并时间相关的任何类型的分子数据（如DNA序列、限制性片段长度多态性[RFLP]或微卫星数据）。蒙特卡罗模拟研究用于分析我们的估计量的行为。我们表明，其中一个估计量（mY）由于无偏差且方差相对较低，似乎适合从分子数据中估计混合比例。然后，我们将其与基于基因频率的两种传统估计量进行比较。结果表明，在广泛的情况下，尤其是对于微卫星数据，mY的偏差小于传统估计量。然而，当亲本群体差异不大时，其方差大于传统估计量。只有当亲本群体已经分离了大约N代且突变率较高时，mY的方差才会小于传统估计量。模拟还表明，应始终研究多个位点以大幅降低方差，并且对于微卫星数据，如果调查足够多的位点，mY的均方误差会迅速小于传统估计量。我们将新的估计量应用于对微卫星数据进行测试的混合狼样犬科动物群体的案例。
