MrBayes - PAUP

joe at removethispart.gs.washington.edu joe at removethispart.gs.washington.edu
Mon Nov 4 13:15:17 EST 2002


In article <aq6b8p$g8s$1 at mercury.hgmp.mrc.ac.uk>,
Wurff, Andre vander <Andre.vanderWurff at wur.nl> wrote:
>we have used MrBayes software on about 400 full length 18S rDNA (1700
>bp)with the TGR model of evolution. With MrBayes, the bootstrap support
>for deeper (and basically all) phylogenetic nodes is strikingly high
>(about 100%). Does anybody have the same experience with MrBayes ? Does
>anybody know some relevant literature on this ? (Our NJ trees with PAUP
>have remarkably low support at deeper nodes).

Let me correct one bit of terminology.  The MrBayes support levels are not
bootstrap support but posterior probabilities.  Your observation is a common
one, and a bit of a mystery.  Quite a few people are doing simulations
to try to figure out why the Bayesian posterior values out of MrBayes are
so much larger than bootstrap values.  There seem to be four possible answers,
and as yet there is no consensus as to which is correct:
   1. Maybe these two numbers are fundamentally different quantities that are
      not even to be compared, and not expected to be similar.  In which case,
      if they are different this is not a problem for either.
   2. Maybe this reflects the well-known bias of high bootstrap P values,
      which are biased downward (this was discovered in 1993 by Zharkikh
      and Li).
   3. Maybe there are some search problems in MrBayes such that it tends to
      get "stuck" on some trees, so that the Markov Chain Monte Carlo
      algorithm reports much higher posterior probabilities of trees than it
      should.  This seems unlikely as Huelsenbeck has allowed for multiple
      "heated" chains which (the Metropolis-Coupled Markov Chain Monte Carlo
      MC^3 method of Geyer) which should enable a reasonable search.
   4. Maybe the high MrBayes posterior probabilities are consequences of the
      particular prior distributions assumed on trees, which assume that
      branch lengths are drawn from a distribution in which they are frequently
      allowed to be very long.  This could then be remedied by altering the
      prior that is used, and MrBayes does allow you some control over that.
I expect that over the next year or two the matter should be cleared up.

-- 
Joe Felsenstein         joe at removethispart.gs.washington.edu
 Department of Genome Sciences, University of Washington,
 Box 357730, Seattle, WA 98195-7730 USA


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