An integrated-likelihood method for estimating genetic differentiation between populations
An integrated-likelihood method for estimating genetic differentiation between populations
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DOI：
10.1534/genetics.106.055350
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发表时间：
2006-08-01
期刊：
影响因子：
3.3
通讯作者：
Skaug, Hans Julius
中科院分区：
文献类型：
--
作者：
Kitakado, Toshihide;Kitada, Shuichi;Skaug, Hans Julius
本文的目的是发展一个综合似然（IL）的方法来估计群体之间的遗传分化。传统的最大似然（ML）和伪似然（PL）方法使用等位基因的样本计数可能会导致F-ST的严重低估，这意味着当采样地点的数量很小时，theta = 4 Nm的高估。为了减少遗传分化估计中的这种偏差，我们提出了一种IL方法，在该方法中，种群的平均等位基因频率被视为滋扰参数，并通过整合消除。为了最大化IL函数，我们开发了两种算法，蒙特卡罗EM算法和拉普拉斯近似。我们的仿真研究表明，这里提出的方法优于传统的ML和PL方法的无偏性和精度。IL方法适用于太平洋鲱鱼和非洲象的真实的数据。
The aim of this article is to develop an integrated-likelihood (IL) approach to estimate the genetic differentiation between populations. The conventional maximum-likelihood (ML) and pseudolikelihood (PL) methods that use sample counts of alleles may cause severe underestimations of F-ST, which means overestimations of theta = 4Nm, when the number of sampling localities is small. To reduce such bias in the estimation of genetic differentiation, we propose an IL method in which the mean allele frequencies over populations are regarded as nuisance parameters and are eliminated by integration. To maximize the IL function, we have developed two algorithms, a Monte Carlo EM algorithm and a Laplace approximation. Our simulation studies show that the method proposed here outperforms the conventional ML and PL methods in terms of unbiasedness and precision. The IL method was applied to real data for Pacific herring and African elephants.