一种用于遗传插补和合并推理的非参数HMM
A nonparametric HMM for genetic imputation and coalescent inference
摘要：
遗传序列数据很好地描述了隐马尔可夫模型（HMRM），其中潜在的状态对应于类似的突变模式的集群。统计遗传学的理论表明，这些障碍是不均匀的（它们的转移概率沿染色体变化），并有很大的支持自我转换。我们开发了一个新的非参数模型的基因序列数据，分层狄利克雷过程的基础上，它支持这些自转换和非均匀性。我们的模型提供了一个参数化的遗传过程，这是更简约比其他更一般的非参数模型，以前已被应用于群体遗传学。我们提供截断自由MCMC推断我们的模型使用一个新的辅助抽样方案的贝叶斯非参数迟滞。在一系列的实验中，男性X染色体数据从千基因组计划，也从人口瓶颈的数据模拟，我们显示了我们的模型的好处，在流行的有限模型fastPHASE，这本身可以被看作是一个参数截断我们的模型。我们发现，我们的模型中发现的HMM状态的数量与人口瓶颈中最近的共同祖先的时间相关。这项工作表明了贝叶斯nonparametrics适用于大型和复杂的遗传数据的灵活性。
英文摘要：
Genetic sequence data are well described by hidden Markov models (HMMs) in which latent states correspond to clusters of similar mutation patterns. Theory from statistical genetics suggests that these HMMs are nonhomogeneous (their transition probabilities vary along the chromosome) and have large support for self transitions. We develop a new nonparametric model of genetic sequence data, based on the hierarchical Dirichlet process, which supports these self transitions and nonhomogeneity. Our model provides a parameterization of the genetic process that is more parsimonious than other more general nonparametric models which have previously been applied to population genetics. We provide truncation-free MCMC inference for our model using a new auxiliary sampling scheme for Bayesian nonparametric HMMs. In a series of experiments on male X chromosome data from the Thousand Genomes Project and also on data simulated from a population bottleneck we show the benefits of our model over the popular finite model fastPHASE, which can itself be seen as a parametric truncation of our model. We find that the number of HMM states found by our model is correlated with the time to the most recent common ancestor in population bottlenecks. This work demonstrates the flexibility of Bayesian nonparametrics applied to large and complex genetic data.
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