Efficient Simulation and Likelihood Methods for Non-Neutral Multi-Allele Models
Efficient Simulation and Likelihood Methods for Non-Neutral Multi-Allele Models
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DOI：
10.1089/cmb.2012.0033
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发表时间：
2012-06-01
影响因子：
1.7
通讯作者：
Buzbas, Erkan Ozge
中科院分区：
文献类型：
--
作者：
Joyce, Paul;Genz, Alan;Buzbas, Erkan Ozge
在整个20世纪80年代，西蒙·塔瓦雷对群体遗传学理论做出了许多重大贡献。随着遗传数据，特别是DNA序列变得更加容易获得，需要将人口遗传模型与数据联系起来成为中心问题。Griffiths和Tavare（1994 a，1994 b，1994 c）的开创性工作是最早开发出一种使用完整DNA序列估计群体遗传参数的可能性方法之一。现在，我们正处于基因组学时代，需要扩大方法来处理大量数据集，而Tavare已经引领了新方法的发展。然而，在非中性模型下进行统计推断已被证明是难以捉摸的。为了向Simon Tavare致敬，我们提出了一篇文章，他的工作精神，提供了一种计算上易于处理的方法，用于模拟和分析一类非中性群体遗传模型下的数据。Donnelly、Nordborg和Joyce（DNJ）提出了在一类基于等位基因频率的非中性亲本独立突变模型下近似似然函数和生成样本的计算方法（Donnelly et al.，2001年）。DNJ（2001）在拒绝算法中使用中性模型作为辅助分布来模拟来自非中性模型的等位基因频率样本。然而，中性模型产生的等位基因频率的模式与非中性模型产生的等位基因频率的模式不同，使得拒绝方法效率低下。例如，在某些情况下，DNJ（2001）中的方法需要在接受来自非中性模型的样本之前拒绝10（9）次。我们的方法直接从非中性模型的分布中模拟样本，使模拟方法成为研究可能性行为和对选择强度进行推断的实用工具。
Throughout the 1980s, Simon Tavare made numerous significant contributions to population genetics theory. As genetic data, in particular DNA sequence, became more readily available, a need to connect population-genetic models to data became the central issue. The seminal work of Griffiths and Tavare (1994a, 1994b, 1994c) was among the first to develop a likelihood method to estimate the population-genetic parameters using full DNA sequences. Now, we are in the genomics era where methods need to scale-up to handle massive data sets, and Tavare has led the way to new approaches. However, performing statistical inference under non-neutral models has proved elusive. In tribute to Simon Tavare, we present an article in spirit of his work that provides a computationally tractable method for simulating and analyzing data under a class of non-neutral population-genetic models. Computational methods for approximating likelihood functions and generating samples under a class of allele-frequency based non-neutral parent-independent mutation models were proposed by Donnelly, Nordborg, and Joyce (DNJ) (Donnelly et al., 2001). DNJ (2001) simulated samples of allele frequencies from non-neutral models using neutral models as auxiliary distribution in a rejection algorithm. However, patterns of allele frequencies produced by neutral models are dissimilar to patterns of allele frequencies produced by non-neutral models, making the rejection method inefficient. For example, in some cases the methods in DNJ (2001) require 10(9) rejections before a sample from the non-neutral model is accepted. Our method simulates samples directly from the distribution of non-neutral models, making simulation methods a practical tool to study the behavior of the likelihood and to perform inference on the strength of selection.