An efficient Monte Carlo method for estimating Ne from temporally spaced samples using a coalescent-based likelihood
An efficient Monte Carlo method for estimating Ne from temporally spaced samples using a coalescent-based likelihood
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DOI：
10.1534/genetics.104.038349
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发表时间：
2005-06-01
期刊：
影响因子：
3.3
通讯作者：
Anderson, EC
中科院分区：
文献类型：
--
作者：
Anderson, EC
本文提出了一种有效的重要性抽样方法计算的可能性下的人口的有效规模的合并模型的Berthier等人。以前的计算方法，使用马尔可夫链蒙特卡罗，需要几分钟到几个小时来分析小数据集。这里提出的方法是数量级更快，可以提供一个近似的似然曲线，即使是大型数据集，在几秒钟内。此外，估计似然曲线上的置信区间提供了蒙特卡罗误差的有用估计。仿真结果表明，重要性抽样是稳定的，在广泛的情况下，并表明N-E估计本身表现良好。进一步的模拟表明，N-e估计值附近的95%置信区间是准确的。可下载用于Mac、Windows和Unix/Linux的实现该算法的用户友好型软件。这个计算框架的其他问题的应用进行了讨论。
This article presents an efficient importance-sampling method for computing the likelihood of the effective size of a population under the coalescent model of Berthier et al. Previous computational approaches, using Markov chain Monte Carlo, required many minutes to several hours to analyze small data sets. The approach presented here is orders of magnitude faster and can provide an approximation to the likelihood curve, even for large data sets, in a matter of seconds. Additionally, confidence intervals on the estimated likelihood curve provide a useful estimate of the Monte Carlo error. Simulations show the importance sampling to be stable across a wide range of scenarios and show that the N-e estimator itself performs well. Further simulations show that the 95% confidence intervals around the N-e estimate are accurate. User-friendly software implementing the algorithm for Mac, Windows, and Unix/Linux is available for download. Applications of this computational framework to other problems are discussed.