Distinguishing Migration From Isolation: A Markov Chain Monte Carlo Approach

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Standard

Distinguishing Migration From Isolation : A Markov Chain Monte Carlo Approach. / Nielsen, Rasmus; Wakeley, John.

In: Genetics, Vol. 158, No. 2, 2001, p. 885-896.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Nielsen, R & Wakeley, J 2001, 'Distinguishing Migration From Isolation: A Markov Chain Monte Carlo Approach', Genetics, vol. 158, no. 2, pp. 885-896. <https://www.genetics.org/content/158/2/885>

APA

Nielsen, R., & Wakeley, J. (2001). Distinguishing Migration From Isolation: A Markov Chain Monte Carlo Approach. Genetics, 158(2), 885-896. https://www.genetics.org/content/158/2/885

Vancouver

Nielsen R, Wakeley J. Distinguishing Migration From Isolation: A Markov Chain Monte Carlo Approach. Genetics. 2001;158(2):885-896.

Author

Nielsen, Rasmus ; Wakeley, John. / Distinguishing Migration From Isolation : A Markov Chain Monte Carlo Approach. In: Genetics. 2001 ; Vol. 158, No. 2. pp. 885-896.

Bibtex

@article{1d80349eb77c476e9ed53689ed9e09ee,
title = "Distinguishing Migration From Isolation: A Markov Chain Monte Carlo Approach",
abstract = "A Markov chain Monte Carlo method for estimating the relative effects of migration and isolation on genetic diversity in a pair of populations from DNA sequence data is developed and tested using simulations. The two populations are assumed to be descended from a panmictic ancestral population at some time in the past and may (or may not) after that be connected by migration. The use of a Markov chain Monte Carlo method allows the joint estimation of multiple demographic parameters in either a Bayesian or a likelihood framework. The parameters estimated include the migration rate for each population, the time since the two populations diverged from a common ancestral population, and the relative size of each of the two current populations and of the common ancestral population. The results show that even a single nonrecombining genetic locus can provide substantial power to test the hypothesis of no ongoing migration and/or to test models of symmetric migration between the two populations. The use of the method is illustrated in an application to mitochondrial DNA sequence data from a fish species: the threespine stickleback (Gasterosteus aculeatus).",
author = "Rasmus Nielsen and John Wakeley",
year = "2001",
language = "English",
volume = "158",
pages = "885--896",
journal = "Genetics",
issn = "1943-2631",
publisher = "The Genetics Society of America (GSA)",
number = "2",

}

RIS

TY - JOUR

T1 - Distinguishing Migration From Isolation

T2 - A Markov Chain Monte Carlo Approach

AU - Nielsen, Rasmus

AU - Wakeley, John

PY - 2001

Y1 - 2001

N2 - A Markov chain Monte Carlo method for estimating the relative effects of migration and isolation on genetic diversity in a pair of populations from DNA sequence data is developed and tested using simulations. The two populations are assumed to be descended from a panmictic ancestral population at some time in the past and may (or may not) after that be connected by migration. The use of a Markov chain Monte Carlo method allows the joint estimation of multiple demographic parameters in either a Bayesian or a likelihood framework. The parameters estimated include the migration rate for each population, the time since the two populations diverged from a common ancestral population, and the relative size of each of the two current populations and of the common ancestral population. The results show that even a single nonrecombining genetic locus can provide substantial power to test the hypothesis of no ongoing migration and/or to test models of symmetric migration between the two populations. The use of the method is illustrated in an application to mitochondrial DNA sequence data from a fish species: the threespine stickleback (Gasterosteus aculeatus).

AB - A Markov chain Monte Carlo method for estimating the relative effects of migration and isolation on genetic diversity in a pair of populations from DNA sequence data is developed and tested using simulations. The two populations are assumed to be descended from a panmictic ancestral population at some time in the past and may (or may not) after that be connected by migration. The use of a Markov chain Monte Carlo method allows the joint estimation of multiple demographic parameters in either a Bayesian or a likelihood framework. The parameters estimated include the migration rate for each population, the time since the two populations diverged from a common ancestral population, and the relative size of each of the two current populations and of the common ancestral population. The results show that even a single nonrecombining genetic locus can provide substantial power to test the hypothesis of no ongoing migration and/or to test models of symmetric migration between the two populations. The use of the method is illustrated in an application to mitochondrial DNA sequence data from a fish species: the threespine stickleback (Gasterosteus aculeatus).

M3 - Journal article

C2 - 11404349

AN - SCOPUS:0034967295

VL - 158

SP - 885

EP - 896

JO - Genetics

JF - Genetics

SN - 1943-2631

IS - 2

ER -

ID: 222645186