Inferring admixture proportions from molecular data
Inferring admixture proportions from molecular data
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DOI：
10.1093/oxfordjournals.molbev.a025858
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发表时间：
1998-10-01
影响因子：
10.7
通讯作者：
Excoffier, L
中科院分区：
文献类型：
--
作者：
Bertorelle, G;Excoffier, L
我们在这里推导出两个新的估计混合比例的基础上的聚结的方法，明确考虑到分子信息以及基因频率。这些估计可以应用于任何类型的分子数据（如DNA序列，限制性片段长度多态性[RFLPs]，dr微卫星数据），其中分子多样性的程度与聚结时间有关。蒙特卡洛模拟研究被用来分析我们的估计的行为。我们发现，其中之一（m（Y））似乎适合估计混合物的分子数据，因为它没有偏见和相对较低的方差。然后，我们将其与基于基因频率的两个传统估计进行比较。m（Y）证明在广泛的情况下，特别是对于微卫星数据，比传统的估计值偏差更小。然而，它的方差是大于传统的估计量时，父母的人口是不是很分化。只有当亲本群体分离N代左右且突变率较高时，m（Y）的方差才小于常规估计量的方差。模拟还表明，应始终研究几个位点，以实现方差的急剧减少，并且对于微卫星数据，如果调查足够的位点，m（Y）的均方误差迅速变得小于常规估计值。我们将我们的新估计的情况下，钦佩的类犬科动物种群的微卫星数据进行测试。
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], dr 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 (m(Y)) 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. m(Y) 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 m(Y) 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 m(Y) rapidly becomes smaller than that of conventional estimators if enough loci are surveyed. We apply our new estimator to the case of admired wolflike Canid populations tested for microsatellite data.