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Refining Convergent Rate Analysis with Topology in Mammalian Longevity and Marine Transitions

Treaster, Stephen, Daane, Jacob M, Harris, Matthew P·2021·Molecular Biology and Evolution
DOI 10.1093/molbev/msab226PMC PMC8557430

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Abstract

The quest to map the genetic foundations of phenotypes has been empowered by the modern diversity, quality, and availability of genomic resources. Despite these expanding resources, the abundance of variation within lineages makes it challenging to associate genetic change to specific phenotypes, without an a priori means of isolating the changes from background genomic variation. Evolution provides this means through convergence—that is, the shared variation that may result from replicate evolutionary experiments across independent trait occurrences. To leverage these opportunities, we developed TRACCER: Topologically Ranked Analysis of Convergence via Comparative Evolutionary Rates. Compared to current methods, this software empowers rate convergence analysis by factoring in topological relationships, because genetic variation between phylogenetically proximate trait changes is more likely to be facilitating the trait. Comparisons are performed not with singular branches, but with the complete paths to the most recent common ancestor for each pair of lineages. This ensures that comparisons represent a single context diverging over the same timeframe while obviating the problematic requirement of assigning ancestral states. We applied TRACCER to two case studies: mammalian transitions to marine environments, an unambiguous collection of traits that have independently evolved three times; and the evolution of mammalian longevity, a less delineated trait but with more instances to compare. By factoring in topology, TRACCER identifies highly significant, convergent genetic signals, with important incongruities and statistical resolution when compared to existing approaches. These improvements in sensitivity and specificity of convergence analysis generate refined targets for downstream validation and identification of genotype–phenotype relationships.

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  • Rates-based PhyloG2P methods search for genetic elements that evolve at different rates in foreground and background lineages.

    “PhyloG2P methods based on changes in evolutionary rates search for genetic elements which evolve at different rates in foreground and background lineages.”

    From From Trees to Traits: A Review of Advances in PhyloG2P Methods and Future Directions (2025) · cites this paper as Treaster et al. 2021

  • Rates-based methods can identify genetic elements under positive selection where multiple different mutations cause a similar phenotypic change, which single-site methods may miss.

    “Additionally, in the context of replicated phenotypic transitions, evolutionary rates-based methods can identify genetic elements experiencing positive selection in which multiple different mutations could cause a similar change in phenotype (Fig. 2b), which may not be identified by methods based on replicated changes at single sites (Treaster et al. 2021).”

    From From Trees to Traits: A Review of Advances in PhyloG2P Methods and Future Directions (2025) · cites this paper as Treaster et al. 2021

  • RERconverge requires branches to be assigned as foreground or background through ancestral state reconstruction, which can be challenging for many traits.

    “The first issue is that RERconverge requires branches to be assigned as foreground or background through ancestral state reconstruction, which can be challenging for many traits”

    From From Trees to Traits: A Review of Advances in PhyloG2P Methods and Future Directions (2025) · cites this paper as Treaster et al. 2021

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Metadata

Authors
Treaster, S., Daane, J. M., & Harris, M. P.
Affiliations
Department of Orthopaedic Research, Boston Children’s Hospital, Boston, MA, USA
ORCID
Treaster, Stephen
Venue
Molecular Biology and Evolution
Publisher
Oxford University Press (OUP)
Published
2021-07-29
Volume
38
Issue
11
Pages
5190-5203
ISSN
  • 0737-4038
  • 1537-1719
DOI
10.1093/molbev/msab226
PMC
PMC8557430
Cited by
24
References
3
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
  • American Federation for Aging Research Glenn Foundation Postdoctoral Fellowship
  • National Academy of Medicine Healthy Longevity Catalyst Award (#2000011734)

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