| Analysis of Genealogical Structure |
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The fundamental processes
of population differentiation and speciation are, at their genesis,
the same when viewed from a genealogical perspective. Initially, when
two interbreeding populations begin to diverge from a single
interbreeding population the gene copies within both descendant
populations for any particular locus will share many ancestors in
common. In absence of genetic exchange among these populations, over
time genetic drift leads to sorting of the gene-lineages. As some
gene-lineages proliferate and others go extinct, patterns of exclusive
ancestry within each population evolve. Eventually, gene copies at all
loci within each interbreeding population will evolve to a state of
reciprocal monophyly if the process of genetic drift is unopposed.
From coalescent theory we know the time-frame of this transition will
vary for neutral genes by the stochastic nature of the evolutionary
sampling process and as a function of the inbreeding effective size of
the locus considered. Introgressive hybridization can also create
mismatches when gene flow between divergent populations brings
distinct genes across the boundary of differentiation. It is at these
later time points that delineation of species becomes problematic for
definitional as well as analytical reasons.
Our recent research has focused on developing a novel statistic, the
genealogical sorting index, for the problem of species delineation
and population differentiation.
Conceptually the approach is more consistent with coalescent theory
compared to other methods applied to population differentiation. It
is also more powerful as it uses information inherent in genealogies.
In addition the statistical methodology adeptly accommodates
uncertainty in genealogies by integrating (marginalizing) over
genealogies following other methods applied in parameter estimation in
population genetics using maximum likelihood and Bayesian methods. We
have developed software for calculating our novel statistic, the
genealogical sorting index, and assessing its associated probability
value for hypothesis testing.
Personnel:
Adam L. Bazinet
Michael P. Cummings
Collaborators:
Maile C. Neel
Kerry Shaw
Further Information:
e-mail Michael P. Cummings
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