Math @ Duke

Publications [#69199] of Mark Huber
Papers Published
 D. Hearn and M. Huber, The Ancestral Distance test: A topdown approach to detect correlated evolution in large lineages with missing character data and incomplete phylogenies,
Systematic Biology, vol. 55 no. 5
(October, 2006),
pp. 803817, Taylor & Francis
(last updated on 2007/08/05)
Abstract: We present the ancestral distance test, a new
test to detect correlated evolution between
two binary traits. It is appropriate for use
with phylogenies that lack resolved
subclades, branch lengths, and/or comparative
data. We define the ancestral distance as
the time separating a randomly sampled taxon
from its most recent common ancestor (MRCA)
that has one or more descendants possessing
an independent trait. The sampled taxon
either has (target sample) or lacks
(nontarget sample) a dependent trait.
Modeled as a Markov process, we show that the
distribution of ancestral distances for the
target sample is identical to the nontarget
sample when characters are uncorrelated,
whereas ancestral distances are smaller on
average for the target sample when characters
are correlated. Simulations suggest that the
ancestral distance can be estimated using the
time, total branch length, taxonomic rank, or
number of speciation events between a sampled
taxon and the MRCA. These results are shown
to be robust to deviations from Markov
assumptions. We also provide a Monte Carlo
technique to estimate pvalues when full
resolved phylogenies with branch lengths are
available. Software is available from Hearn.
We apply this Monte Carlo approach to a
published data set.


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