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Deep learning for population genetic inference

Sara Sheehan, Yun S. Song
doi: https://doi.org/10.1101/028175
Sara Sheehan
1Department of Computer Science, Smith College, Northampton, MA, USA
2Computer Science Division, UC Berkeley, Berkeley, CA, USA
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  • For correspondence: ssheehan@smith.edu yss@berkeley.edu
Yun S. Song
2Computer Science Division, UC Berkeley, Berkeley, CA, USA
3Department of Statistics, UC Berkeley, Berkeley, CA, USA
4Department of Integrative Biology, UC Berkeley, Berkeley, CA, USA
5Department of Mathematics, University of Pennsylvania, Philadelphia, PA, USA
6Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
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  • For correspondence: ssheehan@smith.edu yss@berkeley.edu
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Abstract

Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce here a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. In contrast to Approximate Bayesian Computation, another likelihood-free approach widely used in population genetics and other fields, deep learning does not require a distance function on summary statistics or a rejection step, and it is robust to the addition of uninformative statistics. To demonstrate that deep learning can be effectively employed to estimate population genetic parameters and learn informative features of data, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme, likely due in part to the unaccounted impact of selection.

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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 October 02, 2015.
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Deep learning for population genetic inference
Sara Sheehan, Yun S. Song
bioRxiv 028175; doi: https://doi.org/10.1101/028175
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Deep learning for population genetic inference
Sara Sheehan, Yun S. Song
bioRxiv 028175; doi: https://doi.org/10.1101/028175

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