Spatial and temporal heterogeneity in nucleotide sequence evolution
Spatial and temporal heterogeneity in nucleotide sequence evolution
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DOI：
10.1093/molbev/msn119
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发表时间：
2008-08-01
影响因子：
10.7
通讯作者：
Whelan, Simon
中科院分区：
文献类型：
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作者：
Whelan, Simon
核苷酸取代模型对进化过程做出了许多简化的假设，包括同一过程作用于比对中的所有位点和系统发育树上的所有分支。许多研究表明，在现实中的替代过程是异质性的，这种变异性可以引入系统误差到许多形式的系统发育分析。我提出了一个新的严格的方法来描述异质性称为时间隐马尔可夫模型（THMM），它可以区分之间的网站（空间）异质性和之间的血统（时间）异质性。几个版本的THMM应用于16组比对序列，以定量评估不同形式的异质性在其中发挥作用。最一般的THMM提供了最好的适合在所有的数据集检查，在进化过程中普遍的异质性提供了强有力的证据。研究个体形式的异质性提供了进一步的见解。与先前的研究一致，空间发生率异质性（研究中心间发生率[RAS]）被推断为最普遍的异质性形式。有趣的是，RAS似乎是如此占主导地位，未能独立地将其包括在THMM掩盖其他形式的异质性，特别是时间异质性。将RAS转换为THMM揭示了核苷酸组成的大量时空异质性，并在所有比对中对过渡取代的偏倚进行了检查，尽管不同形式的异质性的相对重要性在数据集之间有所不同。此外，通过增加模型的复杂性观察到的模型拟合的改善表明，本研究中使用的THERK并没有捕获数据中发生的所有进化异质性。这些观察结果都表明，目前的测试可能一贯低估的程度发生在数据的时间异质性。最后，检测到的异质性的量和序列之间的分歧水平之间存在薄弱的联系，这表明进化过程中的变异性将是深源性的一个特殊问题。
Models of nucleotide substitution make many simplifying assumptions about the evolutionary process, including that the same process acts on all sites in an alignment and on all branches on the phylogenetic tree. Many studies have shown that in reality the substitution process is heterogeneous and that this variability can introduce systematic errors into many forms of phylogenetic analyses. I propose a new rigorous approach for describing heterogeneity called a temporal hidden Markov model (THMM), which can distinguish between among site (spatial) heterogeneity and among lineage (temporal) heterogeneity. Several versions of the THMM are applied to 16 sets of aligned sequences to quantitatively assess the different forms of heterogeneity acting within them. The most general THMM provides the best fit in all the data sets examined, providing strong evidence of pervasive heterogeneity during evolution. Investigating individual forms of heterogeneity provides further insights. In agreement with previous studies, spatial rate heterogeneity (rates across sites [RAS]) is inferred to be the single most prevalent form of heterogeneity. Interestingly, RAS appears so dominant that failure to independently include it in the THMM masks other forms of heterogeneity, particularly temporal heterogeneity. Incorporating RAS into the THMM reveals substantial temporal and spatial heterogeneity in nucleotide composition and bias toward transition substitution in all alignments examined, although the relative importance of different forms of heterogeneity varies between data sets. Furthermore, the improvements in model fit observed by adding complexity to the model suggest that the THMMs used in this study do not capture all the evolutionary heterogeneity occurring in the data. These observations all indicate that current tests may consistently underestimate the degree of temporal heterogeneity occurring in data. Finally, there is a weak link between the amount of heterogeneity detected and the level of divergence between the sequences, suggesting that variability in the evolutionary process will be a particular problem for deep phylogeny.