Molecular Biology and Evolution, Vol 15, 1298-1311, Copyright © 1998 by Society for Molecular Biology and Evolution
G Bertorelle and L Excoffier
We derive here two new estimators of admixture proportions based on a
coalescent approach that explicitly takes into account molecular
information as well as gene frequencies. These estimators can be applied to
any type of molecular data (such as DNA sequences, restriction fragment
length polymorphisms [RFLPs], or microsatellite data) for which the extent
of molecular diversity is related to coalescent times. Monte Carlo
simulation studies are used to analyze the behavior of our estimators. We
show that one of them (mY) appears suitable for estimating admixture from
molecular data because of its absence of bias and relatively low variance.
We then compare it to two conventional estimators that are based on gene
frequencies. mY proves to be less biased than conventional estimators over
a wide range of situations and especially for microsatellite data. However,
its variance is larger than that of conventional estimators when parental
populations are not very differentiated. The variance of mY becomes smaller
than that of conventional estimators only if parental populations have been
kept separated for about N generations and if the mutation rate is high.
Simulations also show that several loci should always be studied to achieve
a drastic reduction of variance and that, for microsatellite data, the mean
square error of mY rapidly becomes smaller than that of conventional
estimators if enough loci are surveyed. We apply our new estimator to the
case of admixed wolflike Canid populations tested for microsatellite data.
ORIGINAL ARTICLE
Inferring admixture proportions from molecular data
Department of Integrative Biology, University of California, Berkeley, USA.
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