Allele frequency spectra in structured populations: Novel-allele probabilities under the labelled coalescent.
Abstract
We address the effect of population structure on key properties of the Ewens sampling
formula. We use our previously-introduced inductive method for determining exact allele
frequency spectrum (AFS) probabilities under the infinite-allele model of mutation
and population structure for samples of arbitrary size. Fundamental to the sampling
distribution is the novel-allele probability, the probability that given the pattern
of variation in the present sample, the next gene sampled belongs to an as-yet-unobserved
allelic class. Unlike the case for panmictic populations, the novel-allele probability
depends on the AFS of the present sample. We derive a recursion that directly provides
the marginal novel-allele probability across AFSs, obviating the need first to determine
the probability of each AFS. Our explorations suggest that the marginal novel-allele
probability tends to be greater for initial samples comprising fewer alleles and for
sampling configurations in which the next-observed gene derives from a deme different
from that of the majority of the present sample. Comparison to the efficient importance
sampling proposals developed by De Iorio and Griffiths and colleagues indicates that
their approximation for the novel-allele probability generally agrees with the true
marginal, although it may tend to overestimate the marginal in cases in which the
novel-allele probability is high and migration rates are low.
Type
Journal articleSubject
Allele frequency spectrumCoalescence
Ewens sampling formula
Importance sampling
Infinite-allele mutation
Population structure
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https://hdl.handle.net/10161/21884Published Version (Please cite this version)
10.1016/j.tpb.2020.01.002Publication Info
Uyenoyama, Marcy K; Takebayashi, Naoki; & Kumagai, Seiji (2020). Allele frequency spectra in structured populations: Novel-allele probabilities under
the labelled coalescent. Theoretical population biology, 133. pp. 130-140. 10.1016/j.tpb.2020.01.002. Retrieved from https://hdl.handle.net/10161/21884.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Marcy K. Uyenoyama
Professor of Biology
Marcy Uyenoyama studies mechanisms of evolutionary change at the molecular and population
levels. Among the questions under study include the prediction and detection of the
effects of natural selection on genomic structure. A major area of research addresses
the development of maximum-likelihood and Bayesian methods for inferring evolutionary
processes from the pattern of molecular variation. Evolutionary processes currently
under study include characterization of population structure acr

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