SnIPRE: selection inference using a Poisson random effects model.

We present an approach for identifying genes under natural selection using polymorphism and divergence data from synonymous and non-synonymous sites within genes. A generalized linear mixed model is used to model the genome-wide variability among categories of mutations and estimate its functional c...

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Autores principales:	Kirsten E Eilertson, James G Booth, Carlos D Bustamante
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/80fe1bf436b4473d8cd8ba1a9a73c221
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spelling	oai:doaj.org-article:80fe1bf436b4473d8cd8ba1a9a73c2212021-11-18T05:52:39ZSnIPRE: selection inference using a Poisson random effects model.1553-734X1553-735810.1371/journal.pcbi.1002806https://doaj.org/article/80fe1bf436b4473d8cd8ba1a9a73c2212012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23236270/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358We present an approach for identifying genes under natural selection using polymorphism and divergence data from synonymous and non-synonymous sites within genes. A generalized linear mixed model is used to model the genome-wide variability among categories of mutations and estimate its functional consequence. We demonstrate how the model's estimated fixed and random effects can be used to identify genes under selection. The parameter estimates from our generalized linear model can be transformed to yield population genetic parameter estimates for quantities including the average selection coefficient for new mutations at a locus, the synonymous and non-synynomous mutation rates, and species divergence times. Furthermore, our approach incorporates stochastic variation due to the evolutionary process and can be fit using standard statistical software. The model is fit in both the empirical Bayes and Bayesian settings using the lme4 package in R, and Markov chain Monte Carlo methods in WinBUGS. Using simulated data we compare our method to existing approaches for detecting genes under selection: the McDonald-Kreitman test, and two versions of the Poisson random field based method MKprf. Overall, we find our method universally outperforms existing methods for detecting genes subject to selection using polymorphism and divergence data.Kirsten E EilertsonJames G BoothCarlos D BustamantePublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 8, Iss 12, p e1002806 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Kirsten E Eilertson James G Booth Carlos D Bustamante SnIPRE: selection inference using a Poisson random effects model.
description	We present an approach for identifying genes under natural selection using polymorphism and divergence data from synonymous and non-synonymous sites within genes. A generalized linear mixed model is used to model the genome-wide variability among categories of mutations and estimate its functional consequence. We demonstrate how the model's estimated fixed and random effects can be used to identify genes under selection. The parameter estimates from our generalized linear model can be transformed to yield population genetic parameter estimates for quantities including the average selection coefficient for new mutations at a locus, the synonymous and non-synynomous mutation rates, and species divergence times. Furthermore, our approach incorporates stochastic variation due to the evolutionary process and can be fit using standard statistical software. The model is fit in both the empirical Bayes and Bayesian settings using the lme4 package in R, and Markov chain Monte Carlo methods in WinBUGS. Using simulated data we compare our method to existing approaches for detecting genes under selection: the McDonald-Kreitman test, and two versions of the Poisson random field based method MKprf. Overall, we find our method universally outperforms existing methods for detecting genes subject to selection using polymorphism and divergence data.
format	article
author	Kirsten E Eilertson James G Booth Carlos D Bustamante
author_facet	Kirsten E Eilertson James G Booth Carlos D Bustamante
author_sort	Kirsten E Eilertson
title	SnIPRE: selection inference using a Poisson random effects model.
title_short	SnIPRE: selection inference using a Poisson random effects model.
title_full	SnIPRE: selection inference using a Poisson random effects model.
title_fullStr	SnIPRE: selection inference using a Poisson random effects model.
title_full_unstemmed	SnIPRE: selection inference using a Poisson random effects model.
title_sort	snipre: selection inference using a poisson random effects model.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/80fe1bf436b4473d8cd8ba1a9a73c221
work_keys_str_mv	AT kirsteneeilertson snipreselectioninferenceusingapoissonrandomeffectsmodel AT jamesgbooth snipreselectioninferenceusingapoissonrandomeffectsmodel AT carlosdbustamante snipreselectioninferenceusingapoissonrandomeffectsmodel
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SnIPRE: selection inference using a Poisson random effects model.

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