Estimating effective population size from temporally spaced samples with a novel, efficient maximum-likelihood algorithm

Genetics
Tin-Yu J Hui, Austin Burt

Abstract

The effective population size [Formula: see text] is a key parameter in population genetics and evolutionary biology, as it quantifies the expected distribution of changes in allele frequency due to genetic drift. Several methods of estimating [Formula: see text] have been described, the most direct of which uses allele frequencies measured at two or more time points. A new likelihood-based estimator [Formula: see text] for contemporary effective population size using temporal data is developed in this article. The existing likelihood methods are computationally intensive and unable to handle the case when the underlying [Formula: see text] is large. This article tries to work around this problem by using a hidden Markov algorithm and applying continuous approximations to allele frequencies and transition probabilities. Extensive simulations are run to evaluate the performance of the proposed estimator [Formula: see text], and the results show that it is more accurate and has lower variance than previous methods. The new estimator also reduces the computational time by at least 1000-fold and relaxes the upper bound of [Formula: see text] to several million, hence allowing the estimation of larger [Formula: see text]. Finally, w...Continue Reading

References

Mar 15, 1955·Proceedings of the National Academy of Sciences of the United States of America·M Kimura
Aug 28, 2007·Genetics·Per Erik Jorde, Nils Ryman
Jul 1, 2008·Molecular Ecology Resources·Robin S Waples, Chi DO
Sep 1, 1971·Evolution; International Journal of Organic Evolution·Costas B Krimbas, Spyros Tsakas

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Citations

Jun 30, 2016·Heredity·J WangA Caballero
Mar 18, 2020·Genome Biology and Evolution·Michael Lynch, Wei-Chin Ho
Feb 1, 2018·PLoS Genetics·Florian ClementeRenaud Vitalis
May 6, 2021·Molecular Ecology Resources·Tin-Yu J HuiAustin Burt

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Methods Mentioned

BETA
genotyping

Software Mentioned

R
Windows
Fortran
MLNE
LDNE

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