Importance sampling on coalescent histories. II: Subdivided population models
Importance sampling on coalescent histories. II: Subdivided population models
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DOI：
10.1239/aap/1086957580
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发表时间：
2004-06-01
影响因子：
1.2
通讯作者：
Griffiths, RC
中科院分区：
文献类型：
--
作者：
De Iorio, M;Griffiths, RC
De Iorio和Griffiths（2004）开发了一种新的方法，在基因样本的合并历史上构建序贯重要性抽样建议分布，用于通过模拟计算样本中基因类型配置的可能性。该方法是基于近似的扩散过程发生器描述的人口基因频率的分布，导致一个近似的样本分布，并最终重要抽样建议分布。本文应用该方法构造了一个重要抽样算法，用于计算细分群体模型中基因样本的似然性。Stephens和Donnelly（2000）的重要性抽样技术因此被扩展到具有基因类型之间的马尔可夫链突变机制和亚群之间的基因迁移的模型。本文推广了Ewens（1972）在单种群中的抽样公式，导出了在无限多等位基因突变模型中从细分种群中计算基因样本构型似然的算法。研究了无限多位点模型下由DNA序列构建基因树的似然计算和祖先推理。针对细分群体改进了Bahlo和Griffiths（2000）基因树中的Griffiths-Tavare似然计算方法。
De Iorio and Griffiths (2004) developed a new method of constructing sequential importance-sampling proposal distributions on coalescent histories of a sample of genes for computing the likelihood of a type configuration of genes in the sample by simulation. The method is based on approximating the diffusion-process generator describing the distribution of population gene frequencies, leading to an approximate sample distribution and finally to importance-sampling proposal distributions. This paper applies that method to construct an importance-sampling algorithm for computing the likelihood of samples of genes in subdivided population models. The importance-sampling technique of Stephens and Donnelly (2000) is thus extended to models with a Markov chain mutation mechanism between gene types and migration of genes between subpopulations. An algorithm for computing the likelihood of a sample configuration of genes from a subdivided population in an infinitely-many-alleles model of mutation is derived, extending Ewens's (1972) sampling formula in a single population. Likelihood calculation and ancestral inference in gene trees constructed from DNA sequences under the infinitely-many-sites model are also studied. The Griffiths-Tavare method of likelihood calculation in gene trees of Bahlo and Griffiths (2000) is improved for subdivided populations.