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POPS: A Software for Prediction of Population Genetic Structure Using Latent Regression Models Olivier François Eric Y. Durand

Flora Jay 1 Olivier François 1 Eric Y Durand 1 Michael Gb Blum 1
1 TIMC-BCM [?-2015] - Biologie Computationnelle et Mathématique [?-2015]
TIMC [2011-2015] - Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble - UMR 5525 [2011-2015]
Abstract : The software POPS performs inference of population genetic structure using multi-locus genotypic data. Based on a hierarchical Bayesian framework for latent regression models, POPS implements algorithms that improve estimation of individual admixture proportions and cluster membership probabilities by using geographic and environmental information. In addition, POPS defines ancestry distribution models allowing its users to forecast admixture proportion and cluster membership geographic variation under changing environmental conditions. We illustrate a typical use of POPS using data for an alpine plant species, for which POPS predicts changes in spatial population structure assuming a particular scenario of climate change.
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Flora Jay, Olivier François, Eric Y Durand, Michael Gb Blum. POPS: A Software for Prediction of Population Genetic Structure Using Latent Regression Models Olivier François Eric Y. Durand. Journal of Statistical Software, University of California, Los Angeles, 2015, 68 (9), ⟨10.18637/jss.v068.i09⟩. ⟨hal-01256474⟩

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