Advertisement

Journal of Molecular Evolution

, Volume 42, Issue 2, pp 308–312 | Cite as

The effect of topology on estimates of among-site rate variation

  • Jack Sullivan
  • Kent E. Holsinger
  • Chris Simon
Articles

Abstract

Among-site rate variation, as quantified by the gamma-distribution shape parameter,a or α, and the ratio of transition rate to transversion rate (Ts/Tv) influence phylogenetic inference. We examine the effect of topology on estimates of these two parameters in 12S rRNA sequences from nine species of mice belonging to the generaOnychomys andPeromyscus by generating 100 random topologies and estimating these parameters using parsimony and maximum-likelihood methods for each of the random topologies. The parsimony-based estimate ofTs/Tv from the well-corroborated topology falls within the distribution of estimates based on random topologies, whereas the maximum-likelihood estimate ofTs/Tv based on the well-corroborated topology lies well outside the distribution of estimates derived from random topologies. TheTs/Tv ratio derived via maximumlikelihood estimation is three times the parsimony-based estimate, suggesting that parsimony-based estimates are severe underestimates even when the correct topology is used. Both parsimony- and likelihood-based estimates of the gamma-distribution shape parameter (α) are sensitive to topology because the best estimates based on the well-corroborated topology are well outside the distributions of estimates derived from random topologies for both methods. We show that the reason for topology dependence is the presence of long internal branches in the underlying topology.

Key words

Gamma shape parameter Among-site rate variation Phylogeny 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Farris JS (1969) A successive approximations approach to character weighting. Syst Zool 18:374–385Google Scholar
  2. Gaut BS, Lewis PO (1995) Success of maximum likelihood phylogeny inference in the four-taxon case. Mol Biol Evol 12:152–162Google Scholar
  3. Kocher TD, Wilson AC (1991) Sequence evolution of mitochondrial DNA in human and chimpanzees: control region and protein coding region. In: Osawa S, Honjo T, (eds) Evolution of life: fossils, molecules, and culture. Springer, Tokyo, pp 391–413Google Scholar
  4. Kuhner MK, Felsenstein J (1994) A simulation of phylogeny algorithms under equal and unequal evolutionary rates. Mol Biol Evol 11:459–468Google Scholar
  5. Maddison WP, Maddison DR (1992) MacClade: analysis of phylogeny and character evolution. Version 3.0. Sinauer Associates, Sunderland, MAGoogle Scholar
  6. Sullivan J, Holsinger KE, Simon C (1995) Among-site rate variation and phylogenetic analysis of 12S rRNA in Sigmodontine rodents. Mol Biol Evol 12:988–1001Google Scholar
  7. Tateno Y, Takezaki N, Nei M (1994) Relative efficiencies of the maximum likelihood, neighbor joining, and maximum parsimony methods when substitution rate varies with site. Mol Biol Evol 11:261–277Google Scholar
  8. Wakeley J (1994) Substitution-rate variation among sites and the estimation of transition bias. Mol Biol Evol 11:426–442Google Scholar
  9. Williams PL, Fitch WM (1990) Phylogeny determination using the dynamically weighted parsimony method. Methods Enzymol 183:615–627Google Scholar
  10. Yang Z (1994) Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: approximate methods. J Mol Evol 39:306–414Google Scholar
  11. Yang Z, Goldman N, Friday A (1994) Comparison of models for nucleotide substitution used in maximum-likelihood phylogenetic estimation. Mol Biol Evol 11:316–324Google Scholar

Copyright information

© Springer-Verlag New York Inc. 1996

Authors and Affiliations

  • Jack Sullivan
    • 1
  • Kent E. Holsinger
    • 1
  • Chris Simon
    • 1
  1. 1.Department of Ecology and Evolutionary Biology, U-43University of ConnecticutStorrsUSA

Personalised recommendations