SPEAKER: Dr. James Degnan, Dept. of Human Genetics, University of
Michigan

TITLE: Coalescent Consequences for Consensus Cladograms

ABSTRACT

Consensus methods provide a useful strategy for combining
information from a collection of gene trees.  To investigate the
theoretical properties of consensus trees that would be obtained from
large numbers of loci evolving according to a basic evolutionary
model, we construct consensus trees from independent gene trees that
occur in proportion to gene tree probabilities derived from coalescent
theory.  We consider majority-rule, rooted triple ($R^*$), and greedy
consensus trees constructed from finite samples of known gene trees
from independent loci and the consensus trees that are obtained as
sample sizes grow indefinitely large.  Our results show that for some
combinations of species tree branch lengths, increasing the number of
independent loci can make the majority-rule consensus tree more likely
to be at least partially unresolved and the greedy consensus tree less
likely to match the species tree. However, the probability that the
$R^*$ consensus tree has the species tree topology approach 1 as the
number of gene trees approaches infinity.  Although the greedy
consensus algorithm can be the quickest to converge on the correct
species tree when increasing the number of gene trees it can also be
positively misleading, the majority-rule consensus tree is not a
misleading estimator of the species tree topology, and the $R^*$
consensus tree is a statistically consistent estimator of the species
tree topology.