Monte Carlo evaluation of the likelihood for N(e) from temporally spaced samples.
Monte Carlo evaluation of the likelihood for N(e) from temporally spaced samples.
复制标题
对时间间隔样本的 N(e) 的可能性进行蒙特卡罗评估。
DOI：
10.1093/genetics/156.4.2109
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发表时间：
2000
期刊：
影响因子：
3.3
通讯作者：
Thompson,EA
中科院分区：
文献类型：
--
作者：
Anderson,EC;Williamson,EG;Thompson,EA
种群的有效大小是保护和管理的重要数量。有效大小可以从群体中采集的时间间隔遗传样本中观察到的等位基因频率的变化来估计。虽然存在基于矩的估计，最近威廉姆森和斯莱特金证明了最大似然方法的优势，他们适用于双等位基因遗传标记的数据。然而，他们的计算方法并不适用于多等位基因标记的数据，因为在这种情况下，精确评估可能性是不可能的，需要对潜在变量进行棘手的求和。我们提出了一种蒙特卡罗方法来计算的可能性与多等位基因标记的数据。为了提高计算效率，我们的方法依赖于一个重要的抽样分布构造的一个向前向后的方法。我们描述了蒙特卡罗公式和重要性抽样函数，然后演示了它们在模拟和真实的数据集上的使用。
A population’s effective size is an important quantity for conservation and management. The effective size may be estimated from the change of allele frequencies observed in temporally spaced genetic samples taken from the population. Though moment-based estimators exist, recently Williamson and Slatkin demonstrated the advantages of a maximum-likelihood approach that they applied to data on diallelic genetic markers. Their computational methods, however, do not extend to data on multiallelic markers, because in such cases exact evaluation of the likelihood is impossible, requiring an intractable sum over latent variables. We present a Monte Carlo approach to compute the likelihood with data on multiallelic markers. So as to be computationally efficient, our approach relies on an importance-sampling distribution constructed by a forward-backward method. We describe the Monte Carlo formulation and the importance-sampling function and then demonstrate their use on both simulated and real datasets.