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Bayesian Inference of Natural Selection from Allele Frequency Time Series

Joshua G. Schraiber, Steven N. Evans, Montgomery Slatkin
doi: https://doi.org/10.1101/037200
Joshua G. Schraiber
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Steven N. Evans
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Montgomery Slatkin
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Abstract

The advent of accessible ancient DNA technology now allows the direct ascertainment of allele frequencies in ancestral populations, thereby enabling the use of allele frequency time series to detect and estimate natural selection. Such direct observations of allele frequency dynamics are expected to be more powerful than inferences made using patterns of linked neutral variation obtained from modern individuals. We developed a Bayesian method to make use of allele frequency time series data and infer the parameters of general diploid selection, along with allele age, in non-equilibrium populations. We introduce a novel path augmentation approach, in which we use Markov chain Monte Carlo to integrate over the space of allele frequency trajectories consistent with the observed data. Using simulations, we show that this approach has good power to estimate selection coefficients and allele age. Moreover, when applying our approach to data on horse coat color, we find that ignoring a relevant demographic history can significantly bias the results of inference. Our approach is made available in a C++ software package.

Footnotes

  • JGS supported by NSF grant DBI-1402120, SNE supported in part by NSF grant DMS-0907630, NSF grant DMS-1512933, and NIH grant 1R01GM109454-01, MS supported by NIH grant R01-GM40282.

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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-ND 4.0 International license.
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Posted January 19, 2016.
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Bayesian Inference of Natural Selection from Allele Frequency Time Series
Joshua G. Schraiber, Steven N. Evans, Montgomery Slatkin
bioRxiv 037200; doi: https://doi.org/10.1101/037200
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Bayesian Inference of Natural Selection from Allele Frequency Time Series
Joshua G. Schraiber, Steven N. Evans, Montgomery Slatkin
bioRxiv 037200; doi: https://doi.org/10.1101/037200

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