Determining the evolutionary history of gene families
Determining the evolutionary history of gene families
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DOI：
10.1093/bioinformatics/btr592
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发表时间：
2012-01-01
期刊：
影响因子：
5.8
通讯作者：
Lovell, Simon C.
中科院分区：
文献类型：
--
作者：
Ames, Ryan M.;Money, Daniel;Lovell, Simon C.
动机：最近对群体中个体的大规模研究表明，在许多基因家族中存在广泛的拷贝数变异。此外，越来越多的证据表明，基因拷贝数的变化可以引起大量的表型效应。在某些情况下，这些变化已被证明是适应性的。这些观察结果表明，要充分了解生物功能的进化，需要了解基因获得和基因丢失。准确，强大的进化模型的收益和损失events，因此，requires.Results：我们已经开发出加权简约和最大似然法推断收益和损失events。为了测试这些方法，我们使用马尔可夫模型的增益和损失，以模拟数据与已知的属性。我们研究三种模型：一个简单的出生死亡模型，一个单一的利率模型和出生死亡的新息模型的参数估计从果蝇基因组数据。我们发现，对于所有模拟，基于最大似然的方法对于重建系统发育树上的重复事件数量非常准确，并且最大似然和加权简约法对于重建祖先状态具有相似的准确性。我们的实现对不同的模型参数是鲁棒的，并提供祖先状态和增益和损失事件的数量的准确推断。对于祖先重建，我们推荐加权简约法，因为它具有与最大似然法相似的精度，但速度更快。对于推断个体基因丢失或获得事件的数量，最大似然法明显更准确，尽管计算成本更高。
Motivation: Recent large-scale studies of individuals within a population have demonstrated that there is widespread variation in copy number in many gene families. In addition, there is increasing evidence that the variation in gene copy number can give rise to substantial phenotypic effects. In some cases, these variations have been shown to be adaptive. These observations show that a full understanding of the evolution of biological function requires an understanding of gene gain and gene loss. Accurate, robust evolutionary models of gain and loss events are, therefore, required.Results: We have developed weighted parsimony and maximum likelihood methods for inferring gain and loss events. To test these methods, we have used Markov models of gain and loss to simulate data with known properties. We examine three models: a simple birth-death model, a single rate model and a birth-death innovation model with parameters estimated from Drosophila genome data. We find that for all simulations maximum likelihood-based methods are very accurate for reconstructing the number of duplication events on the phylogenetic tree, and that maximum likelihood and weighted parsimony have similar accuracy for reconstructing the ancestral state. Our implementations are robust to different model parameters and provide accurate inferences of ancestral states and the number of gain and loss events. For ancestral reconstruction, we recommend weighted parsimony because it has similar accuracy to maximum likelihood, but is much faster. For inferring the number of individual gene loss or gain events, maximum likelihood is noticeably more accurate, albeit at greater computational cost.