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RERconverge: an R package for associating evolutionary rates with convergent traits
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Abstract
Motivation: When different lineages of organisms independently adapt to similar environments, selection often acts repeatedly upon the same genes, leading to signatures of convergent evolutionary rate shifts at these genes. With the increasing availability of genome sequences for organisms displaying a variety of convergent traits, the ability to identify genes with such convergent rate signatures would enable new insights into the molecular basis of these traits. Results: Here we present the R package RERconverge, which tests for association between relative evolutionary rates of genes and the evolution of traits across a phylogeny. RERconverge can perform associations with binary and continuous traits, and it contains tools for visualization and enrichment analyses of association results. Availability and implementation: RERconverge source code, documentation and a detailed usage walk-through are freely available at https://github.com/nclark-lab/RERconverge. Datasets for mammals, Drosophila and yeast are available at https://bit.ly/2J2QBnj. Supplementary information: Supplementary data are available at Bioinformatics online.
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RERconverge is an R package for the genome-wide association of convergent evolutionary rate shifts with convergent phenotypes.
“we introduce categorical methods within RERconverge, an R package for the genome-wide association of convergent evolutionary rate shifts with convergent phenotypes (Kowalczyk et al. 2019).”
From RERconverge Expansion: Using Relative Evolutionary Rates to Study Complex Categorical Trait Evolution (2024) · cites this paper as Kowalczyk et al. 2019
PCOC is available as a container and RERconverge is available as an R package.
“such as PCOC being available as a container (Rey et al. 2018) or RERconverge being available as an R package Kowalczyk et al. 2019”
From From Trees to Traits: A Review of Advances in PhyloG2P Methods and Future Directions (2025) · cites this paper as Kowalczyk et al. 2019
Previous RERconverge ancestral trait reconstruction functionality was only available for binary or continuous traits and used an approximate maximum parsimony approach for binary traits and estimation of maximum likelihood states for continuous traits.
“Previous RERconverge ancestral trait reconstruction functionality was only available for binary or continuous traits and used an approximate maximum parsimony approach for binary traits and estimation of maximum likelihood states for continuous traits (Maddison 1989; Kowalczyk et al. 2019; Revell 2024).”
From RERconverge Expansion: Using Relative Evolutionary Rates to Study Complex Categorical Trait Evolution (2024) · cites this paper as Kowalczyk et al. 2019
Using a likelihood ratio test, the all rates different (ARD) model was selected to infer the ancestral states.
“Using a likelihood ratio test, as implemented in the expanded categorical RERconverge (Pagel 1994; Kowalczyk et al. 2019), we selected the all rates different (ARD) model to infer the ancestral states”
From RERconverge Expansion: Using Relative Evolutionary Rates to Study Complex Categorical Trait Evolution (2024) · cites this paper as Kowalczyk et al. 2019
RERconverge quantifies the rate of substitution along a branch as an evolutionary rate and computes relative evolutionary rates (RERs) that reflect whether a given gene is evolving slower or faster than expected along a branch of the phylogeny.
“The rate of substitution along a branch is quantified as an evolutionary rate and, by correcting for factors affecting rates at all genes or on all branches of a phylogeny, we can compute relative evolutionary rates (RERs) that reflect whether a given gene is evolving slower or faster than expected along a branch of the phylogeny (Kowalczyk et al. 2019).”
From RERconverge Expansion: Using Relative Evolutionary Rates to Study Complex Categorical Trait Evolution (2024) · cites this paper as Kowalczyk et al. 2019
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Cites
Bayesian Detection of Convergent Rate Changes of Conserved Noncoding Elements on Phylogenetic Trees
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Sorted by citations
Fukushima, K., & Pollock, D. D. (2023) · Nature Ecology & Evolution · doi:10.1038/s41559-022-01932-7 · Cited by 55
A phylogenetic approach to comparative genomics
Dewar, A. E., Belcher, L. J., & West, S. A. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-024-00803-0 · Cited by 41
Redlich, R., Kowalczyk, A., Tene, M., Sestili, H. H., Foley, K., Saputra, E., Clark, N., Chikina, M., Meyer, W. K., & Pfenning, A. R. (2024) · Molecular Biology and Evolution · doi:10.1093/molbev/msae210 · Cited by 14
Self-citationFrom Trees to Traits: A Review of Advances in PhyloG2P Methods and Future Directions
Macdonald, A. R., James, M. E., Mitchell, J. D., & Holland, B. R. (2025) · Genome Biology and Evolution · doi:10.1093/gbe/evaf150 · Cited by 5
Thomas, G. W. C., Gemmell, P., Shakya, S. B., Hu, Z., Liu, J. S., Sackton, T. B., & Edwards, S. V. (2024) · Integrative And Comparative Biology · doi:10.1093/icb/icae056 · Cited by 5
Metadata
- Authors
- Kowalczyk, A., Meyer, W. K., Partha, R., Mao, W., Clark, N. L., & Chikina, M.
- Affiliations
- Department of Computational and Systems Biology, University of Pittsburgh , Pittsburgh, PA 15213, USA
- ORCID
- Meyer, Wynn K
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-06-13
- Volume
- 35
- Issue
- 22
- Pages
- 4815-4817
- ISSN
- 1367-4803
- 1367-4811
- Cited by
- 147
- References
- 1
- Language
- en
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Funders
- National Institutes of Health (R01 HG009299-01A1, T32 EB009403)
- University of Pittsburgh Center for Research Computing
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