On the use of bootstrapped topologies in coalescent-based Bayesian MCMC inference: a comparison of estimation and computational efficiencies.

Published online

Journal Article

Coalescent-based Bayesian Markov chain Monte Carlo (MCMC) inference generates estimates of evolutionary parameters and their posterior probability distributions. As the number of sequences increases, the length of time taken to complete an MCMC analysis increases as well. Here, we investigate an approach to distribute the MCMC analysis across a cluster of computers. To do this, we use bootstrapped topologies as fixed genealogies, perform a single MCMC analysis on each genealogy without topological rearrangements, and pool the results across all MCMC analyses. We show, through simulations, that although the standard MCMC performs better than the bootstrap-MCMC at estimating the effective population size (scaled by mutation rate), the bootstrap-MCMC returns better estimates of growth rates. Additionally, we find that our bootstrap-MCMC analyses are, on average, 37 times faster for equivalent effective sample sizes.

Full Text

Duke Authors

Cited Authors

  • Rodrigo, AG; Tsai, P; Shearman, H

Published Date

  • July 31, 2009

Published In

Volume / Issue

  • 5 /

Start / End Page

  • 97 - 105

PubMed ID

  • 19812730

Electronic International Standard Serial Number (EISSN)

  • 1176-9343


  • eng

Conference Location

  • United States