Department of Economics and Business Economics

Non-parametric estimation of population size changes from the site frequency spectrum

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Non-parametric estimation of population size changes from the site frequency spectrum. / Waltoft, Berit Lindum; Hobolth, Asger.

In: Statistical Applications in Genetics and Molecular Biology, Vol. 17, No. 3, 11.06.2018.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

Waltoft, BL & Hobolth, A 2018, 'Non-parametric estimation of population size changes from the site frequency spectrum', Statistical Applications in Genetics and Molecular Biology, vol. 17, no. 3. https://doi.org/10.1515/sagmb-2017-0061

APA

CBE

MLA

Waltoft, Berit Lindum and Asger Hobolth. "Non-parametric estimation of population size changes from the site frequency spectrum". Statistical Applications in Genetics and Molecular Biology. 2018. 17(3). https://doi.org/10.1515/sagmb-2017-0061

Vancouver

Waltoft BL, Hobolth A. Non-parametric estimation of population size changes from the site frequency spectrum. Statistical Applications in Genetics and Molecular Biology. 2018 Jun 11;17(3). https://doi.org/10.1515/sagmb-2017-0061

Author

Waltoft, Berit Lindum ; Hobolth, Asger. / Non-parametric estimation of population size changes from the site frequency spectrum. In: Statistical Applications in Genetics and Molecular Biology. 2018 ; Vol. 17, No. 3.

Bibtex

@article{2015b95f4c0f4035b6d08fc32dae79a0,
title = "Non-parametric estimation of population size changes from the site frequency spectrum",
abstract = "Changes in population size is a useful quantity for understanding the evolutionary history of a species. Genetic variation within a species can be summarized by the site frequency spectrum (SFS). For a sample of size n, the SFS is a vector of length n - 1 where entry i is the number of sites where the mutant base appears i times and the ancestral base appears n - i times. We present a new method, CubSFS, for estimating the changes in population size of a panmictic population from an observed SFS. First, we provide a straightforward proof for the expression of the expected site frequency spectrum depending only on the population size. Our derivation is based on an eigenvalue decomposition of the instantaneous coalescent rate matrix. Second, we solve the inverse problem of determining the changes in population size from an observed SFS. Our solution is based on a cubic spline for the population size. The cubic spline is determined by minimizing the weighted average of two terms, namely (i) the goodness of fit to the observed SFS, and (ii) a penalty term based on the smoothness of the changes. The weight is determined by cross-validation. The new method is validated on simulated demographic histories and applied on unfolded and folded SFS from 26 different human populations from the 1000 Genomes Project.",
author = "Waltoft, {Berit Lindum} and Asger Hobolth",
year = "2018",
month = "6",
day = "11",
doi = "10.1515/sagmb-2017-0061",
language = "English",
volume = "17",
journal = "Statistical Applications in Genetics and Molecular Biology",
issn = "1544-6115",
publisher = "Walterde Gruyter GmbH",
number = "3",

}

RIS

TY - JOUR

T1 - Non-parametric estimation of population size changes from the site frequency spectrum

AU - Waltoft, Berit Lindum

AU - Hobolth, Asger

PY - 2018/6/11

Y1 - 2018/6/11

N2 - Changes in population size is a useful quantity for understanding the evolutionary history of a species. Genetic variation within a species can be summarized by the site frequency spectrum (SFS). For a sample of size n, the SFS is a vector of length n - 1 where entry i is the number of sites where the mutant base appears i times and the ancestral base appears n - i times. We present a new method, CubSFS, for estimating the changes in population size of a panmictic population from an observed SFS. First, we provide a straightforward proof for the expression of the expected site frequency spectrum depending only on the population size. Our derivation is based on an eigenvalue decomposition of the instantaneous coalescent rate matrix. Second, we solve the inverse problem of determining the changes in population size from an observed SFS. Our solution is based on a cubic spline for the population size. The cubic spline is determined by minimizing the weighted average of two terms, namely (i) the goodness of fit to the observed SFS, and (ii) a penalty term based on the smoothness of the changes. The weight is determined by cross-validation. The new method is validated on simulated demographic histories and applied on unfolded and folded SFS from 26 different human populations from the 1000 Genomes Project.

AB - Changes in population size is a useful quantity for understanding the evolutionary history of a species. Genetic variation within a species can be summarized by the site frequency spectrum (SFS). For a sample of size n, the SFS is a vector of length n - 1 where entry i is the number of sites where the mutant base appears i times and the ancestral base appears n - i times. We present a new method, CubSFS, for estimating the changes in population size of a panmictic population from an observed SFS. First, we provide a straightforward proof for the expression of the expected site frequency spectrum depending only on the population size. Our derivation is based on an eigenvalue decomposition of the instantaneous coalescent rate matrix. Second, we solve the inverse problem of determining the changes in population size from an observed SFS. Our solution is based on a cubic spline for the population size. The cubic spline is determined by minimizing the weighted average of two terms, namely (i) the goodness of fit to the observed SFS, and (ii) a penalty term based on the smoothness of the changes. The weight is determined by cross-validation. The new method is validated on simulated demographic histories and applied on unfolded and folded SFS from 26 different human populations from the 1000 Genomes Project.

U2 - 10.1515/sagmb-2017-0061

DO - 10.1515/sagmb-2017-0061

M3 - Journal article

C2 - 29886455

VL - 17

JO - Statistical Applications in Genetics and Molecular Biology

JF - Statistical Applications in Genetics and Molecular Biology

SN - 1544-6115

IS - 3

ER -