Coestimation of recombination, substitution and molecular adaptation rates by approximate Bayesian computation

J. S. Lopes*, M. Arenas, D. Posada, M. A. Beaumont

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

20 Citations (Scopus)

Abstract

The estimation of parameters in molecular evolution may be biased when some processes are not considered. For example, the estimation of selection at the molecular level using codon-substitution models can have an upward bias when recombination is ignored. Here we address the joint estimation of recombination, molecular adaptation and substitution rates from coding sequences using approximate Bayesian computation (ABC). We describe the implementation of a regression-based strategy for choosing subsets of summary statistics for coding data, and show that this approach can accurately infer recombination allowing for intracodon recombination breakpoints, molecular adaptation and codon substitution rates. We demonstrate that our ABC approach can outperform other analytical methods under a variety of evolutionary scenarios. We also show that although the choice of the codon-substitution model is important, our inferences are robust to a moderate degree of model misspecification. In addition, we demonstrate that our approach can accurately choose the evolutionary model that best fits the data, providing an alternative for when the use of full-likelihood methods is impracticable. Finally, we applied our ABC method to co-estimate recombination, substitution and molecular adaptation rates from 24 published human immunodeficiency virus 1 coding data sets.

Original languageEnglish
Pages (from-to)255-264
Number of pages10
JournalHeredity
Volume112
Issue number3
DOIs
Publication statusPublished - Mar 2014

Keywords

  • ABC
  • dN/dS
  • HIV-1
  • molecular adaptation
  • mutation
  • recombination

Fingerprint Dive into the research topics of 'Coestimation of recombination, substitution and molecular adaptation rates by approximate Bayesian computation'. Together they form a unique fingerprint.

Cite this