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A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks

Jeffrey Chan, Valerio Perrone, Jeffrey P. Spence, Paul A. Jenkins, Sara Mathieson, Yun S. Song
doi: https://doi.org/10.1101/267211
Jeffrey Chan
University of California, Berkeley,
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  • For correspondence: chanjed@berkeley.edu
Valerio Perrone
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Jeffrey P. Spence
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Paul A. Jenkins
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Sara Mathieson
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Yun S. Song
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Abstract

An explosion of high-throughput DNA sequencing in the past decade has led to a surge of interest in population-scale inference with whole-genome data. Recent work in population genetics has centered on designing inference methods for relatively simple model classes, and few scalable general-purpose inference techniques exist for more realistic, complex models. To achieve this, two inferential challenges need to be addressed: (1) population data are exchangeable, calling for methods that efficiently exploit the symmetries of the data, and (2) computing likelihoods is intractable as it requires integrating over a set of correlated, extremely high-dimensional latent variables. These challenges are traditionally tackled by likelihood-free methods that use scientific simulators to generate datasets and reduce them to hand-designed, permutation-invariant summary statistics, often leading to inaccurate inference. In this work, we develop an exchangeable neural network that performs summary statistic-free, likelihood-free inference. Our frame-work can be applied in a black-box fashion across a variety of simulation-based tasks, both within and outside biology. We demonstrate the power of our approach on the recombination hotspot testing problem, outperforming the state-of-the-art.

Footnotes

  • 32nd Conference on Neural Information Processing Systems (NIPS 2018), Montréal, Canada.

Copyright 
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 November 05, 2018.
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A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks
Jeffrey Chan, Valerio Perrone, Jeffrey P. Spence, Paul A. Jenkins, Sara Mathieson, Yun S. Song
bioRxiv 267211; doi: https://doi.org/10.1101/267211
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A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks
Jeffrey Chan, Valerio Perrone, Jeffrey P. Spence, Paul A. Jenkins, Sara Mathieson, Yun S. Song
bioRxiv 267211; doi: https://doi.org/10.1101/267211

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