Dataset Information

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

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 framework 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.

SUBMITTER: Chan J

PROVIDER: S-EPMC7687905 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Publications

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

Chan Jeffrey J Perrone Valerio V Spence Jeffrey P JP Jenkins Paul A PA Mathieson Sara S Song Yun S YS

Advances in neural information processing systems 20181201

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 efficien ...[more]

PMID: 33244210

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Dataset Information

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

Publications

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

Similar Datasets

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