统计遗传学中各研究中心的一般化率异质性
Generalising rate heterogeneity across sites in statistical phylogenetics
摘要：
系统发生学使用分子序列数据的比对来了解与物种相关的进化树。沿着分支，序列演化建模使用连续时间马尔可夫过程，其特征在于由瞬时速率矩阵。早期的模型假设相同的速率矩阵在对齐的所有站点的取代，忽略了进化压力的变化。系统发育推断和模型拟合的实质性改善，通过增加这些模型与乘法随机效应，描述了选择性约束的变化的结果，并允许网站以不同的速度线性缩放基线率矩阵的演变。出于这一开创性的工作，我们考虑使用二次，而不是线性，变换的扩展。由此产生的模型允许在一个站点的不同类型的点突变的选择性系数的变化，除了在选择性约束的变化。我们得到的扩展模型的属性。对于某些非平稳过程，扩展给出了一个模型，允许在序列组成的变化，跨网站和类群。我们采用贝叶斯方法，描述了MCMC算法的后验推理，并提供软件。我们的二次模型适用于跨越生命树的路线，并与站点均匀和线性模型。
英文摘要：
Phylogenetics uses alignments of molecular sequence data to learn about evolutionary trees relating species. Along branches, sequence evolution is modelled using a continuous-time Markov process characterised by an instantaneous rate matrix. Early models assumed the same rate matrix governed substitutions at all sites of the alignment, ignoring variation in evolutionary pressures. Substantial improvements in phylogenetic inference and model fit were achieved by augmenting these models with multiplicative random effects that describe the result of variation in selective constraints and allow sites to evolve at different rates which linearly scale a baseline rate matrix. Motivated by this pioneering work, we consider an extension using a quadratic, rather than linear, transformation. The resulting models allow for variation in the selective coefficients of different types of point mutation at a site in addition to variation in selective constraints. We derive properties of the extended models. For certain non-stationary processes, the extension gives a model that allows variation in sequence composition both across sites and taxa. We adopt a Bayesian approach, describe an MCMC algorithm for posterior inference and provide software. Our quadratic models are applied to alignments spanning the tree of life and compared with site-homogeneous and linear models.
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