Phylogenetic Permulations: A Statistically Rigorous Approach to Measure Confidence in Associations in a Phylogenetic Context.
Phylogenetic Permulations: A Statistically Rigorous Approach to Measure Confidence in Associations in a Phylogenetic Context.
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DOI：
10.1093/molbev/msab068
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发表时间：
2021-06-25
影响因子：
10.7
通讯作者：
Chikina M
中科院分区：
文献类型：
--
作者：
Saputra E;Kowalczyk A;Cusick L;Clark N;Chikina M
许多进化比较方法试图确定表型性状之间或性状和基因型之间的关联，通常以推断它们之间的潜在功能关系为目标。比较基因组学的方法，旨在这一目标的措施之间的关联，在遗传水平上的进化变化与性状进化收敛跨越系统发育谱系。然而，这些方法具有复杂的统计行为，这些统计行为受到非平凡且通常未知的混杂因素的影响。因此，在解释这些方法的输出时使用标准统计分析会导致潜在的不准确结论。在这里，我们介绍了系统发育排列，一种新的统计策略，结合系统发育模拟和排列来计算准确的，无偏的P值从系统发育的方法。通过经验生成空表型，置换从给定的系统发育方法构建P值的空期望。随后，基于经验零期望直接计算在数据中给定相关性结构的情况下捕获真实统计置信度的经验P值。我们通过分析二进制和连续表型，包括海洋，地下和长寿的大型哺乳动物表型，检查permulation方法的性能。我们的研究结果表明，permulations提高了系统发育分析的统计能力，并正确校准声明的信心拒绝复杂的零分布，同时保持或提高富集的已知功能相关的表型。我们还发现，permulations细化途径富集分析，通过纠正基因的非独立性。我们的研究结果表明，permulations是一个强大的工具，提高统计置信度的系统发育分析的结论时，参数空是未知的。
Many evolutionary comparative methods seek to identify associations between phenotypic traits or between traits and genotypes, often with the goal of inferring potential functional relationships between them. Comparative genomics methods aimed at this goal measure the association between evolutionary changes at the genetic level with traits evolving convergently across phylogenetic lineages. However, these methods have complex statistical behaviors that are influenced by nontrivial and oftentimes unknown confounding factors. Consequently, using standard statistical analyses in interpreting the outputs of these methods leads to potentially inaccurate conclusions. Here, we introduce phylogenetic permulations, a novel statistical strategy that combines phylogenetic simulations and permutations to calculate accurate, unbiased P values from phylogenetic methods. Permulations construct the null expectation for P values from a given phylogenetic method by empirically generating null phenotypes. Subsequently, empirical P values that capture the true statistical confidence given the correlation structure in the data are directly calculated based on the empirical null expectation. We examine the performance of permulation methods by analyzing both binary and continuous phenotypes, including marine, subterranean, and long-lived large-bodied mammal phenotypes. Our results reveal that permulations improve the statistical power of phylogenetic analyses and correctly calibrate statements of confidence in rejecting complex null distributions while maintaining or improving the enrichment of known functions related to the phenotype. We also find that permulations refine pathway enrichment analyses by correcting for nonindependence in gene ranks. Our results demonstrate that permulations are a powerful tool for improving statistical confidence in the conclusions of phylogenetic analysis when the parametric null is unknown.
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