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POPS: A Software for Prediction of Population Genetic Structure Using Latent Regression Models

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  • Jay, Flora
  • François, Olivier
  • Durand, Eric Y.
  • Blum, Michael G. B.

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.

Suggested Citation

  • Jay, Flora & François, Olivier & Durand, Eric Y. & Blum, Michael G. B., 2015. "POPS: A Software for Prediction of Population Genetic Structure Using Latent Regression Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(i09).
    • Handle: RePEc:jss:jstsof:v:068:i09
    DOI: http://hdl.handle.net/10.18637/jss.v068.i09
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    2. Hwan Chung & Brian P. Flaherty & Joseph L. Schafer, 2006. "Latent class logistic regression: application to marijuana use and attitudes among high school seniors," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 723-743, October.
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