Estimating Effective Population Size from Temporally Spaced Samples with a Novel, Efficient Maximum-Likelihood Algorithm

The effective population size Ne 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 Ne have been described, the most direct of which uses allele frequencies...

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Main Authors:	Hui, Tin-Yu J., Burt, Austin
Format:	Online
Language:	English
Published:	Genetics Society of America 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4423369/

id	pubmed-4423369
recordtype	oai_dc
spelling	pubmed-44233692015-05-08 Estimating Effective Population Size from Temporally Spaced Samples with a Novel, Efficient Maximum-Likelihood Algorithm Hui, Tin-Yu J. Burt, Austin Investigations The effective population size Ne 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 Ne have been described, the most direct of which uses allele frequencies measured at two or more time points. A new likelihood-based estimator NB^ 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 Ne 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 NB^, 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 Ne to several million, hence allowing the estimation of larger Ne. Finally, we demonstrate how this algorithm can cope with nonconstant Ne scenarios and be used as a likelihood-ratio test to test for the equality of Ne throughout the sampling horizon. An R package “NB” is now available for download to implement the method described in this article. Genetics Society of America 2015-05 2015-03-05 /pmc/articles/PMC4423369/ /pubmed/25747459 http://dx.doi.org/10.1534/genetics.115.174904 Text en Copyright © 2015 by the Genetics Society of America Available freely online through the author-supported open access option.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Hui, Tin-Yu J. Burt, Austin
spellingShingle	Hui, Tin-Yu J. Burt, Austin Estimating Effective Population Size from Temporally Spaced Samples with a Novel, Efficient Maximum-Likelihood Algorithm
author_facet	Hui, Tin-Yu J. Burt, Austin
author_sort	Hui, Tin-Yu J.
title	Estimating Effective Population Size from Temporally Spaced Samples with a Novel, Efficient Maximum-Likelihood Algorithm
title_short	Estimating Effective Population Size from Temporally Spaced Samples with a Novel, Efficient Maximum-Likelihood Algorithm
title_full	Estimating Effective Population Size from Temporally Spaced Samples with a Novel, Efficient Maximum-Likelihood Algorithm
title_fullStr	Estimating Effective Population Size from Temporally Spaced Samples with a Novel, Efficient Maximum-Likelihood Algorithm
title_full_unstemmed	Estimating Effective Population Size from Temporally Spaced Samples with a Novel, Efficient Maximum-Likelihood Algorithm
title_sort	estimating effective population size from temporally spaced samples with a novel, efficient maximum-likelihood algorithm
description	The effective population size Ne 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 Ne have been described, the most direct of which uses allele frequencies measured at two or more time points. A new likelihood-based estimator NB^ 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 Ne 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 NB^, 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 Ne to several million, hence allowing the estimation of larger Ne. Finally, we demonstrate how this algorithm can cope with nonconstant Ne scenarios and be used as a likelihood-ratio test to test for the equality of Ne throughout the sampling horizon. An R package “NB” is now available for download to implement the method described in this article.
publisher	Genetics Society of America
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4423369/
_version_	1613220592077504512

Estimating Effective Population Size from Temporally Spaced Samples with a Novel, Efficient Maximum-Likelihood Algorithm

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