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Inferring population size history from large samples of genome wide molecular data - an approximate Bayesian computation approach

Simon Boitard, Willy Rodríguez, Flora Jay, Stefano Mona, Frédéric Austerlitz
doi: https://doi.org/10.1101/036178
Simon Boitard
1UMR 7205 Institut de Systématique, Evolution et Biodiversité, Ecole Pratique des Hautes Etudes & Muséum National d’Histoire Naturelle & CNRS & Université Pierre et Marie Curie, Paris, France
2GABI, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, France
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Willy Rodríguez
3UMR 5219, Institut de Mathématiques de Toulouse, Université de Toulouse & CNRS, France
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Flora Jay
4UMR 7206 Eco-anthropologie et Ethnobiologie, Muséum National d’Histoire Naturelle & CNRS & Université Paris Diderot, Paris, France.
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Stefano Mona
1UMR 7205 Institut de Systématique, Evolution et Biodiversité, Ecole Pratique des Hautes Etudes & Muséum National d’Histoire Naturelle & CNRS & Université Pierre et Marie Curie, Paris, France
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Frédéric Austerlitz
4UMR 7206 Eco-anthropologie et Ethnobiologie, Muséum National d’Histoire Naturelle & CNRS & Université Paris Diderot, Paris, France.
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Abstract

Inferring the ancestral dynamics of effective population size is a long-standing question in population genetics, which can now be tackled much more accurately thanks to the massive genomic data available in many species. Several promising methods that take advantage of whole-genome sequences have been recently developed in this context. However, they can only be applied to rather small samples, which limits their ability to estimate recent population size history. Besides, they can be very sensitive to sequencing or phasing errors. Here we introduce a new approximate Bayesian computation approach named PopSizeABC that allows estimating the evolution of the effective population size through time, using a large sample of complete genomes. This sample is summarized using the folded allele frequency spectrum and the average zygotic linkage disequilibrium at different bins of physical distance, two classes of statistics that are widely used in population genetics and can be easily computed from unphased and unpolarized SNP data. Our approach provides accurate estimations of past population sizes, from the very first generations before present back to the expected time to the most recent common ancestor of the sample, as shown by simulations under a wide range of demographic scenarios. When applied to samples of 15 or 25 complete genomes in four cattle breeds (Angus, Fleckvieh, Holstein and Jersey), PopSizeABC revealed a series of population declines, related to historical events such as domestication or modern breed creation. We further highlight that our approach is robust to sequencing errors, provided summary statistics are computed from SNPs with common alleles.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted January 07, 2016.
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Inferring population size history from large samples of genome wide molecular data - an approximate Bayesian computation approach
Simon Boitard, Willy Rodríguez, Flora Jay, Stefano Mona, Frédéric Austerlitz
bioRxiv 036178; doi: https://doi.org/10.1101/036178
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Inferring population size history from large samples of genome wide molecular data - an approximate Bayesian computation approach
Simon Boitard, Willy Rodríguez, Flora Jay, Stefano Mona, Frédéric Austerlitz
bioRxiv 036178; doi: https://doi.org/10.1101/036178

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