Evaluating the Performance of Widely Used Phylogenetic Models for Gene Expression Evolution.
Evaluating the Performance of Widely Used Phylogenetic Models for Gene Expression Evolution.
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DOI：
10.1093/gbe/evad211
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发表时间：
2023-12-01
影响因子：
3.3
通讯作者：
Pennell, Matt
中科院分区：
文献类型：
--
作者：
Dimayacyac, Jose Rafael;Wu, Shanyun;Jiang, Daohan;Pennell, Matt
系统发育比较方法越来越多地用于测试进化过程的假设，驱动物种之间的基因表达的分歧。然而，它是未知的数量表型性状的系统发育模型的分布假设是现实的表达数据，重要的是，基因表达的系统发育比较研究的结论的可靠性可能取决于数据是否被很好地描述所选择的模型。为了评估这一点，我们首先将几个性状进化的系统发育模型与8个先前发表的比较表达数据集拟合，这些数据集总共包含54，774个基因，具有145，927个独特的基因-组织组合。使用先前开发的方法，然后我们评估了集合的最佳模型在绝对（而不仅仅是相对）意义上描述数据的程度。首先，我们发现，Ornstein-Uhlenbeck模型，其中表达值被约束在一个最佳的，是66%的基因组织组合的首选模型。其次，我们发现，对于61%的基因-组织组合，发现该组的最佳拟合模型表现良好;其余的被发现表现不佳，至少有一个我们检查的检验统计量。第三，我们发现，当简单的模型表现不佳时，这似乎通常是未能充分考虑进化速率的异质性的结果。我们主张模型性能的评估应该成为系统发育比较表达研究的常规组成部分，这样做可以提高推断的可靠性，并激发新模型的发展。
Phylogenetic comparative methods are increasingly used to test hypotheses about the evolutionary processes that drive divergence in gene expression among species. However, it is unknown whether the distributional assumptions of phylogenetic models designed for quantitative phenotypic traits are realistic for expression data and importantly, the reliability of conclusions of phylogenetic comparative studies of gene expression may depend on whether the data is well described by the chosen model. To evaluate this, we first fit several phylogenetic models of trait evolution to 8 previously published comparative expression datasets, comprising a total of 54,774 genes with 145,927 unique gene–tissue combinations. Using a previously developed approach, we then assessed how well the best model of the set described the data in an absolute (not just relative) sense. First, we find that Ornstein–Uhlenbeck models, in which expression values are constrained around an optimum, were the preferred models for 66% of gene–tissue combinations. Second, we find that for 61% of gene–tissue combinations, the best-fit model of the set was found to perform well; the rest were found to be performing poorly by at least one of the test statistics we examined. Third, we find that when simple models do not perform well, this appears to be typically a consequence of failing to fully account for heterogeneity in the rate of the evolution. We advocate that assessment of model performance should become a routine component of phylogenetic comparative expression studies; doing so can improve the reliability of inferences and inspire the development of novel models.
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