An Approximate Markov Model for the Wright–Fisher Diffusion and Its Application to Time Series Data
An Approximate Markov Model for the Wright–Fisher Diffusion and Its Application to Time Series Data
复制标题
Wright-Fisher 扩散的近似马尔可夫模型及其在时间序列数据中的应用
作者：
A. Ferrer;Christoph Leuenberger;J. Jensen;Daniel Wegmann
在群体遗传学中，从遗传数据中对选择和人口统计学进行联合而准确的推断被认为是一个特别具有挑战性的问题，因为这两个过程可能导致非常相似的遗传多样性模式。然而，通过观察等位基因频率在多个时间点上的变化，可以获得用于解开这些效应的附加信息。这样的数据在实验进化研究中很常见，在古代和现代样本的比较中也很常见。然而，利用这些信息在计算上具有挑战性，特别是在考虑多位点数据集时。为了克服这些问题，我们为扩散过程引入了一种新的离散近似，称为平均过渡时间近似，它保留了潜在连续扩散过程的长期行为。然后，我们在经典Wright-Fisher模型下从时间序列数据推断选择和人口统计的特殊情况下推导出这个近似值，并证明我们的近似值非常适合描述等位基因随时间的轨迹，即使只使用少数状态。然后，我们开发了一种贝叶斯推理方法，可以高精度地联合推断种群大小和位点特异性选择系数，并进一步扩展该模型来推断测序错误率和突变率。我们最终将我们的方法应用于最近关于流感病毒耐药性进化的实验数据，确定可能的选择目标，并找到比以前报道的更大的病毒种群规模的证据。
The joint and accurate inference of selection and demography from genetic data is considered a particularly challenging question in population genetics, since both process may lead to very similar patterns of genetic diversity. However, additional information for disentangling these effects may be obtained by observing changes in allele frequencies over multiple time points. Such data are common in experimental evolution studies, as well as in the comparison of ancient and contemporary samples. Leveraging this information, however, has been computationally challenging, particularly when considering multilocus data sets. To overcome these issues, we introduce a novel, discrete approximation for diffusion processes, termed mean transition time approximation, which preserves the long-term behavior of the underlying continuous diffusion process. We then derive this approximation for the particular case of inferring selection and demography from time series data under the classic Wright–Fisher model and demonstrate that our approximation is well suited to describe allele trajectories through time, even when only a few states are used. We then develop a Bayesian inference approach to jointly infer the population size and locus-specific selection coefficients with high accuracy and further extend this model to also infer the rates of sequencing errors and mutations. We finally apply our approach to recent experimental data on the evolution of drug resistance in influenza virus, identifying likely targets of selection and finding evidence for much larger viral population sizes than previously reported.
DOI：
10.1214/14-aoas764
发表时间：
2014-12
期刊：
The annals of applied statistics
影响因子：
--
作者：
Steinrücken M;Bhaskar A;Song YS
通讯作者：
Song YS
使用最大似然根据等位基因频率的时间变化来估计种群大小。
影响因子：
3.3
作者：
Williamson,EG;Slatkin,M
通讯作者：
Slatkin,M