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Phylogenetic Permulations: A Statistically Rigorous Approach to Measure Confidence in Associations in a Phylogenetic Context

Saputra, Elysia, Kowalczyk, Amanda, Cusick, Luisa, Clark, Nathan, Chikina, Maria·2021·Molecular Biology and Evolution
DOI 10.1093/molbev/msab068PMC PMC8233500

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Abstract

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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  • RERconverge uses permulations, which generate null phenotype values using simulations restricted by the observed phenotype distribution to overcome the limitation of simulations.

    “RERconverge uses permulations, which generate null phenotype values using simulations restricted by the observed phenotype distribution to overcome that limitation of simulations.”

    From RERconverge Expansion: Using Relative Evolutionary Rates to Study Complex Categorical Trait Evolution (2024) · cites this paper as Saputra et al. 2021

  • RERconverge provides a built-in method, known as permulations, for the correction of P-values for phylogenetic dependence and other sources of systematic variation leading to nonindependence in the data.

    “The package also provides a built-in method, known as permulations, for the correction of P-values for phylogenetic dependence and other sources of systematic variation leading to nonindependence in the data (Saputra et al. 2021).”

    From RERconverge Expansion: Using Relative Evolutionary Rates to Study Complex Categorical Trait Evolution (2024) · cites this paper as Saputra et al. 2021

  • Binary permulations maintain the same number of foreground species and the same structural relationships between foreground species.

    “For instance, binary permulations maintain the same number of foreground species and the same structural relationships between foreground species (Saputra et al. 2021).”

    From RERconverge Expansion: Using Relative Evolutionary Rates to Study Complex Categorical Trait Evolution (2024) · cites this paper as Saputra et al. 2021

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Metadata

Authors
Saputra, E., Kowalczyk, A., Cusick, L., Clark, N., & Chikina, M.
Affiliations
  • Joint Carnegie Mellon University – University of Pittsburgh PhD Program in Computational Biology, Pittsburgh, PA, USA
  • Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, USA
  • Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
ORCID
Saputra, Elysia
Venue
Molecular Biology and Evolution
Publisher
Oxford University Press (OUP)
Published
2021-03-19
Volume
38
Issue
7
Pages
3004-3021
ISSN
1537-1719
DOI
10.1093/molbev/msab068
PMC
PMC8233500
Cited by
41
References
7
Language
en
Licence
creativecommons.org/licenses/by-nc/4.0/ (vor)
Funders
National Institutes of Health (R01 HG009299)

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