Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
POPS: A Software for Prediction of Population Genetic Structure Using Latent Regression Models | Jay | Journal of Statistical Software
Authors: Flora Jay, Olivier François, Eric Y. Durand, Michael G. B. Blum
Title: POPS: A Software for Prediction of Population Genetic Structure Using Latent Regression Models
Abstract: The software POPS performs inference of population genetic structure using multilocus 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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Paper: POPS: A Software for Prediction of Population Genetic Structure Using Latent Regression Models     Download PDF (Downloads: 715)
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POPS-1.2.1_sources.tgz: POPS source code Download (Downloads: 40; 1MB)
v68i09-replication.zip: Replication materials Download (Downloads: 24; 924KB)

DOI: 10.18637/jss.v068.i09

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Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.