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dc.contributor.authorCornuet, Jean-Marie
dc.contributor.authorRobert, Christian P.
dc.contributor.authorPudlo, Pierre
dc.contributor.authorGuillemaud, Thomas
dc.contributor.authorMarin, Jean-Michel
dc.contributor.authorLombaert, Eric
dc.contributor.authorEstoup, Arnaud
dc.date.accessioned2013-03-06T15:35:00Z
dc.date.available2013-03-06T15:35:00Z
dc.date.issued2012
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/11085
dc.language.isoenen
dc.subjectGeneticen
dc.subjectModelsen
dc.subjectPopulationen
dc.subjectGeneticsen
dc.subjectGenetic Markersen
dc.subjectComputational Biologyen
dc.subjectBiostatisticsen
dc.subjectBeetlesen
dc.subjectAnimalsen
dc.subject.ddc519en
dc.titleEstimation of demo-genetic model probabilities with Approximate Bayesian Computation using linear discriminant analysis on summary statistics.en
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenComparison of demo-genetic models using Approximate Bayesian Computation (ABC) is an active research field. Although large numbers of populations and models (i.e. scenarios) can be analysed with ABC using molecular data obtained from various marker types, methodological and computational issues arise when these numbers become too large. Moreover, Robert et al. (Proceedings of the National Academy of Sciences of the United States of America, 2011, 108, 15112) have shown that the conclusions drawn on ABC model comparison cannot be trusted per se and required additional simulation analyses. Monte Carlo inferential techniques to empirically evaluate confidence in scenario choice are very time-consuming, however, when the numbers of summary statistics (Ss) and scenarios are large. We here describe a methodological innovation to process efficient ABC scenario probability computation using linear discriminant analysis (LDA) on Ss before computing logistic regression. We used simulated pseudo-observed data sets (pods) to assess the main features of the method (precision and computation time) in comparison with traditional probability estimation using raw (i.e. not LDA transformed) Ss. We also illustrate the method on real microsatellite data sets produced to make inferences about the invasion routes of the coccinelid Harmonia axyridis. We found that scenario probabilities computed from LDA-transformed and raw Ss were strongly correlated. Type I and II errors were similar for both methods. The faster probability computation that we observed (speed gain around a factor of 100 for LDA-transformed Ss) substantially increases the ability of ABC practitioners to analyse large numbers of pods and hence provides a manageable way to empirically evaluate the power available to discriminate among a large set of complex scenarios.en
dc.relation.isversionofjnlnameMolecular Ecology Resources
dc.relation.isversionofjnlvol12en
dc.relation.isversionofjnlissue5en
dc.relation.isversionofjnldate2012
dc.relation.isversionofjnlpages846-855en
dc.relation.isversionofdoihttp://dx.doi.org/10.1111/j.1755-0998.2012.03153.xen
dc.relation.isversionofjnlpublisherBlackwellen
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen


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