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Deep learning for population size history inference: design, comparison and combination with approximate Bayesian computation

View ORCID ProfileThéophile Sanchez, View ORCID ProfileJean Cury, Guillaume Charpiat, View ORCID ProfileFlora Jay
doi: https://doi.org/10.1101/2020.01.20.910539
Théophile Sanchez
Laboratoire de Recherche en Informatique, CNRS UMR 8623, Université Paris-Sud, Université Paris-Saclay, Inria, Orsay, France
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  • For correspondence: theophile.sanchez@inria.fr flora.jay@lri.fr
Jean Cury
Laboratoire de Recherche en Informatique, CNRS UMR 8623, Université Paris-Sud, Université Paris-Saclay, Inria, Orsay, France
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Guillaume Charpiat
Laboratoire de Recherche en Informatique, CNRS UMR 8623, Université Paris-Sud, Université Paris-Saclay, Inria, Orsay, France
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Flora Jay
Laboratoire de Recherche en Informatique, CNRS UMR 8623, Université Paris-Sud, Université Paris-Saclay, Inria, Orsay, France
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  • ORCID record for Flora Jay
  • For correspondence: theophile.sanchez@inria.fr flora.jay@lri.fr
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ABSTRACT

For the past decades, simulation-based likelihood-free inference methods have enabled to address numerous population genetics problems. As the richness and amount of simulated and real genetic data keep increasing, the field has a strong opportunity to tackle tasks that current methods hardly solve. However, high data dimensionality forces most methods to summarize large genomic datasets into a relatively small number of handcrafted features (summary statistics). Here we propose an alternative to summary statistics, based on the automatic extraction of relevant information using deep learning techniques. Specifically, we design artificial neural networks (ANNs) that take as input single nucleotide polymorphic sites (SNPs) found in individuals sampled from a single population and infer the past effective population size history. First, we provide guidelines to construct artificial neural networks that comply with the intrinsic properties of SNP data such as invariance to permutation of haplotypes, long scale interactions between SNPs and variable genomic length. Thanks to a Bayesian hyperparameter optimization procedure, we evaluate the performances of multiple networks and compare them to well established methods like Approximate Bayesian Computation (ABC). Even without the expert knowledge of summary statistics, our approach compares fairly well to an ABC based on handcrafted features. Furthermore we show that combining deep learning and ABC can improve performances while taking advantage of both frameworks. Finally, we apply our approach to reconstruct the effective population size history of cattle breed populations.

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  • https://gitlab.inria.fr/ml_genetics/public/DNA_DNA

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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 4.0 International license.
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Posted January 20, 2020.
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Deep learning for population size history inference: design, comparison and combination with approximate Bayesian computation
Théophile Sanchez, Jean Cury, Guillaume Charpiat, Flora Jay
bioRxiv 2020.01.20.910539; doi: https://doi.org/10.1101/2020.01.20.910539
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Deep learning for population size history inference: design, comparison and combination with approximate Bayesian computation
Théophile Sanchez, Jean Cury, Guillaume Charpiat, Flora Jay
bioRxiv 2020.01.20.910539; doi: https://doi.org/10.1101/2020.01.20.910539

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