Fast Inference of Individual Admixture Coefficients Using Geographic Data

Kevin Caye 1 Flora Jay 2, 3, 4 Olivier Michel 5 Olivier François 1
2 BioInfo - LRI - Bioinformatique (LRI)
LRI - Laboratoire de Recherche en Informatique
4 TAU - TAckling the Underspeficied
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
GIPSA-DIS - Département Images et Signal
Abstract : Accurately evaluating the distribution of genetic ancestry across geographic space is one of the main questions addressed by evolutionary biologists. This question has been commonly addressed through the application of Bayesian estimation programs allowing their users to estimate individual admixture proportions and allele frequencies among putative ancestral populations. Following the explosion of high-throughput sequenc-ing technologies, several algorithms have been proposed to cope with computational burden generated by the massive data in those studies. In this context, incorporating geographic proximity in ancestry estimation algorithms is an open statistical and computational challenge. In this study, we introduce new algorithms that use geographic information to estimate ancestry proportions and ancestral genotype frequencies from population genetic data. Our algorithms combine matrix factorization methods and spatial statistics to provide estimates of ancestry matrices based on least-squares approximation. We demonstrate the benefit of using spatial algorithms through extensive computer simulations, and we provide an example of application of our new algorithms to a set of spatially referenced samples for the plant species Arabidopsis thaliana. Without loss of statistical accuracy, the new algorithms exhibit runtimes that are much shorter than those observed for previously developed spatial methods. Our algorithms are implemented in the R package, tess3r.
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Article dans une revue
Annals Of Applied Statistics, Institute Mathematical Statistics, 2018, 〈〉
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Soumis le : samedi 6 janvier 2018 - 02:35:13
Dernière modification le : samedi 27 octobre 2018 - 01:15:23
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  • HAL Id : hal-01676712, version 1


Kevin Caye, Flora Jay, Olivier Michel, Olivier François. Fast Inference of Individual Admixture Coefficients Using Geographic Data. Annals Of Applied Statistics, Institute Mathematical Statistics, 2018, 〈〉. 〈hal-01676712〉



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