Estimating effective population size and mutation rate from sequence data using Metropolis-Hastings sampling.
Estimating effective population size and mutation rate from sequence data using Metropolis-Hastings sampling.
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发表时间：
1995-08
期刊：
影响因子：
3.3
通讯作者：
M. Kuhner;Jon A Yamato;J. Felsenstein
中科院分区：
文献类型：
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作者：
M. Kuhner;Jon A Yamato;J. Felsenstein
我们提出了一种基于分子序列群体样本对参数4N mu（有效群体大小乘以每个位点突变率，或theta）进行最大似然估计的新方法。我们使用Metropolis-Hastings马尔可夫链蒙特卡罗方法对家谱进行抽样，抽样比例与它们相对于数据的可能性和相对于聚结分布的先验概率的乘积成比例。必须选择一个特定的theta值来生成聚结分布，但是得到的树可以用来评估其他theta值处的可能性，从而生成似然曲线。这个过程集中在那些贡献大部分可能性的谱系上取样，允许基于相对较小的样本估计有意义的可能性曲线。该方法可以潜在地扩展到涉及不同种群规模、重组和迁移的情况。
We present a new way to make a maximum likelihood estimate of the parameter 4N mu (effective population size times mutation rate per site, or theta) based on a population sample of molecular sequences. We use a Metropolis-Hastings Markov chain Monte Carlo method to sample genealogies in proportion to the product of their likelihood with respect to the data and their prior probability with respect to a coalescent distribution. A specific value of theta must be chosen to generate the coalescent distribution, but the resulting trees can be used to evaluate the likelihood at other values of theta, generating a likelihood curve. This procedure concentrates sampling on those genealogies that contribute most of the likelihood, allowing estimation of meaningful likelihood curves based on relatively small samples. The method can potentially be extended to cases involving varying population size, recombination, and migration.