Reconstructing population histories from single nucleotide polymorphism data.
Reconstructing population histories from single nucleotide polymorphism data.
复制标题
从单核苷酸多态性数据重建群体历史。
DOI：
10.1093/molbev/msq236
复制
发表时间：
2011
影响因子：
10.7
通讯作者：
J. Corander
中科院分区：
文献类型：
--
作者：
J. Siren;P. Marttinen;J. Corander
群体遗传学包括一个强大的理论和应用研究传统的多个人口过程，形状遗传变异存在于一个物种。当几个不同的种群存在于当前世代时，通常很自然地考虑它们从一个单一的祖先种群中分化的模式。近年来，基于分子数据推断这种种群历史一直是一个深入的研究课题。最常见的方法是使用结合理论来模拟从当前种群中采样的个体的系谱。这种方法能够比较几种不同的进化情景，并估计人口参数。然而，他们的主要限制是巨大的计算复杂性与人口的间接建模，这限制了应用程序的小数据集。在这里，我们提出了一种新的贝叶斯方法推断人口的历史，从不相关的单核苷酸多态性，这也是适用于数据集窝藏大量的个人从不同的人群。我们使用近似的中性Wright-Fisher扩散模型随机波动的等位基因频率。人口的历史被建模为二叉根树，代表不同人口的分歧的历史顺序。分析，数值和蒙特卡罗积分技术的组合被用于推理。我们的方法的一个特别重要的特点是，它提供了直观的统计不确定性的措施与计算的估计，这可能是完全缺乏在这种情况下的替代方法。模拟和真实的数据集的分析说明了我们的方法的潜力。
Population genetics encompasses a strong theoretical and applied research tradition on the multiple demographic processes that shape genetic variation present within a species. When several distinct populations exist in the current generation, it is often natural to consider the pattern of their divergence from a single ancestral population in terms of a binary tree structure. Inference about such population histories based on molecular data has been an intensive research topic in the recent years. The most common approach uses coalescent theory to model genealogies of individuals sampled from the current populations. Such methods are able to compare several different evolutionary scenarios and to estimate demographic parameters. However, their major limitation is the enormous computational complexity associated with the indirect modeling of the demographies, which limits the application to small data sets. Here, we propose a novel Bayesian method for inferring population histories from unlinked single nucleotide polymorphisms, which is applicable also to data sets harboring large numbers of individuals from distinct populations. We use an approximation to the neutral Wright-Fisher diffusion to model random fluctuations in allele frequencies. The population histories are modeled as binary rooted trees that represent the historical order of divergence of the different populations. A combination of analytical, numerical, and Monte Carlo integration techniques are utilized for the inferences. A particularly important feature of our approach is that it provides intuitive measures of statistical uncertainty related with the estimates computed, which may be entirely lacking for the alternative methods in this context. The potential of our approach is illustrated by analyses of both simulated and real data sets.
影响因子：
10.7
作者：
Albrechtsen, Anders;Nielsen, Finn Cilius;Nielsen, Rasmus
通讯作者：
Nielsen, Rasmus
DOI：
--
发表时间：
--
期刊：
--
影响因子：
--
作者：
Jun Z. Li;D. Absher;Hua Tang;Audrey M. Southwick;A. Casto;Sohini Ramachandran;H. Cann;G. Barsh
通讯作者：
Jun Z. Li;D. Absher;Hua Tang;Audrey M. Southwick;A. Casto;Sohini Ramachandran;H. Cann;G. Barsh
使用多位点基因型数据推断群体结构：连锁位点和相关等位基因频率。
影响因子：
3.3
作者：
Falush,Daniel;Stephens,Matthew;Pritchard,JonathanK
通讯作者：
Pritchard,JonathanK