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March 2018 Fast inference of individual admixture coefficients using geographic data
Kevin Caye, Flora Jay, Olivier Michel, Olivier François
Ann. Appl. Stat. 12(1): 586-608 (March 2018). DOI: 10.1214/17-AOAS1106

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 sequencing 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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Kevin Caye. Flora Jay. Olivier Michel. Olivier François. "Fast inference of individual admixture coefficients using geographic data." Ann. Appl. Stat. 12 (1) 586 - 608, March 2018. https://doi.org/10.1214/17-AOAS1106

Information

Received: 1 October 2016; Revised: 1 September 2017; Published: March 2018
First available in Project Euclid: 9 March 2018

zbMATH: 06894719
MathSciNet: MR3773406
Digital Object Identifier: 10.1214/17-AOAS1106

Keywords: Ancestry estimation algorithms , fast algorithms , genotypic data , geographic data

Rights: Copyright © 2018 Institute of Mathematical Statistics

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Vol.12 • No. 1 • March 2018
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