Amount of information needed for model choice in Approximate Bayesian Computation.

Approximate Bayesian Computation (ABC) has become a popular technique in evolutionary genetics for elucidating population structure and history due to its flexibility. The statistical inference framework has benefited from significant progress in recent years. In population genetics, however, its ou...

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Autores principales:	Michael Stocks, Mathieu Siol, Martin Lascoux, Stéphane De Mita
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c56e3f1ff1024541a1ea45d37baa1d4f
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spelling	oai:doaj.org-article:c56e3f1ff1024541a1ea45d37baa1d4f2021-11-11T08:21:48ZAmount of information needed for model choice in Approximate Bayesian Computation.1932-620310.1371/journal.pone.0099581https://doaj.org/article/c56e3f1ff1024541a1ea45d37baa1d4f2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24959900/?tool=EBIhttps://doaj.org/toc/1932-6203Approximate Bayesian Computation (ABC) has become a popular technique in evolutionary genetics for elucidating population structure and history due to its flexibility. The statistical inference framework has benefited from significant progress in recent years. In population genetics, however, its outcome depends heavily on the amount of information in the dataset, whether that be the level of genetic variation or the number of samples and loci. Here we look at the power to reject a simple constant population size coalescent model in favor of a bottleneck model in datasets of varying quality. Not only is this power dependent on the number of samples and loci, but it also depends strongly on the level of nucleotide diversity in the observed dataset. Whilst overall model choice in an ABC setting is fairly powerful and quite conservative with regard to false positives, detecting weaker bottlenecks is problematic in smaller or less genetically diverse datasets and limits the inferences possible in non-model organism where the amount of information regarding the two models is often limited. Our results show it is important to consider these limitations when performing an ABC analysis and that studies should perform simulations based on the size and nature of the dataset in order to fully assess the power of the study.Michael StocksMathieu SiolMartin LascouxStéphane De MitaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 6, p e99581 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Michael Stocks Mathieu Siol Martin Lascoux Stéphane De Mita Amount of information needed for model choice in Approximate Bayesian Computation.
description	Approximate Bayesian Computation (ABC) has become a popular technique in evolutionary genetics for elucidating population structure and history due to its flexibility. The statistical inference framework has benefited from significant progress in recent years. In population genetics, however, its outcome depends heavily on the amount of information in the dataset, whether that be the level of genetic variation or the number of samples and loci. Here we look at the power to reject a simple constant population size coalescent model in favor of a bottleneck model in datasets of varying quality. Not only is this power dependent on the number of samples and loci, but it also depends strongly on the level of nucleotide diversity in the observed dataset. Whilst overall model choice in an ABC setting is fairly powerful and quite conservative with regard to false positives, detecting weaker bottlenecks is problematic in smaller or less genetically diverse datasets and limits the inferences possible in non-model organism where the amount of information regarding the two models is often limited. Our results show it is important to consider these limitations when performing an ABC analysis and that studies should perform simulations based on the size and nature of the dataset in order to fully assess the power of the study.
format	article
author	Michael Stocks Mathieu Siol Martin Lascoux Stéphane De Mita
author_facet	Michael Stocks Mathieu Siol Martin Lascoux Stéphane De Mita
author_sort	Michael Stocks
title	Amount of information needed for model choice in Approximate Bayesian Computation.
title_short	Amount of information needed for model choice in Approximate Bayesian Computation.
title_full	Amount of information needed for model choice in Approximate Bayesian Computation.
title_fullStr	Amount of information needed for model choice in Approximate Bayesian Computation.
title_full_unstemmed	Amount of information needed for model choice in Approximate Bayesian Computation.
title_sort	amount of information needed for model choice in approximate bayesian computation.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/c56e3f1ff1024541a1ea45d37baa1d4f
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Amount of information needed for model choice in Approximate Bayesian Computation.

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