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Legofit: estimating population history from genetic data

BACKGROUND: Our current understanding of archaic admixture in humans relies on statistical methods with large biases, whose magnitudes depend on the sizes and separation times of ancestral populations. To avoid these biases, it is necessary to estimate these parameters simultaneously with those desc...

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Autor principal: Rogers, Alan R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6819480/
https://www.ncbi.nlm.nih.gov/pubmed/31660852
http://dx.doi.org/10.1186/s12859-019-3154-1
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author Rogers, Alan R.
author_facet Rogers, Alan R.
author_sort Rogers, Alan R.
collection PubMed
description BACKGROUND: Our current understanding of archaic admixture in humans relies on statistical methods with large biases, whose magnitudes depend on the sizes and separation times of ancestral populations. To avoid these biases, it is necessary to estimate these parameters simultaneously with those describing admixture. Genetic estimates of population histories also confront problems of statistical identifiability: different models or different combinations of parameter values may fit the data equally well. To deal with this problem, we need methods of model selection and model averaging, which are lacking from most existing software. RESULTS: The Legofit software package allows simultaneous estimation of parameters describing admixture, and the sizes and separation times of ancestral populations. It includes facilities for data manipulation, estimation, analysis of residuals, model selection, and model averaging. CONCLUSIONS: Legofit uses genetic data to study the history of a subdivided population. It is unaffected by recent history and can therefore focus on the deep history of population size, subdivision, and admixture. It outperforms several statistical methods that have been widely used to study population history and should be useful in any species for which DNA sequence data is available from several populations.
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spelling pubmed-68194802019-10-31 Legofit: estimating population history from genetic data Rogers, Alan R. BMC Bioinformatics Software BACKGROUND: Our current understanding of archaic admixture in humans relies on statistical methods with large biases, whose magnitudes depend on the sizes and separation times of ancestral populations. To avoid these biases, it is necessary to estimate these parameters simultaneously with those describing admixture. Genetic estimates of population histories also confront problems of statistical identifiability: different models or different combinations of parameter values may fit the data equally well. To deal with this problem, we need methods of model selection and model averaging, which are lacking from most existing software. RESULTS: The Legofit software package allows simultaneous estimation of parameters describing admixture, and the sizes and separation times of ancestral populations. It includes facilities for data manipulation, estimation, analysis of residuals, model selection, and model averaging. CONCLUSIONS: Legofit uses genetic data to study the history of a subdivided population. It is unaffected by recent history and can therefore focus on the deep history of population size, subdivision, and admixture. It outperforms several statistical methods that have been widely used to study population history and should be useful in any species for which DNA sequence data is available from several populations. BioMed Central 2019-10-28 /pmc/articles/PMC6819480/ /pubmed/31660852 http://dx.doi.org/10.1186/s12859-019-3154-1 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Rogers, Alan R.
Legofit: estimating population history from genetic data
title Legofit: estimating population history from genetic data
title_full Legofit: estimating population history from genetic data
title_fullStr Legofit: estimating population history from genetic data
title_full_unstemmed Legofit: estimating population history from genetic data
title_short Legofit: estimating population history from genetic data
title_sort legofit: estimating population history from genetic data
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6819480/
https://www.ncbi.nlm.nih.gov/pubmed/31660852
http://dx.doi.org/10.1186/s12859-019-3154-1
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