RT Journal Article
SR Electronic
T1 A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 267211
DO 10.1101/267211
A1 Chan, Jeffrey
A1 Perrone, Valerio
A1 Spence, Jeffrey P.
A1 Jenkins, Paul A.
A1 Mathieson, Sara
A1 Song, Yun S.
YR 2018
UL http://biorxiv.org/content/early/2018/02/18/267211.abstract
AB Inference for population genetics models is hindered by computationally intractable likelihoods. While this issue is tackled by likelihood-free methods, these approaches typically rely on handcrafted summary statistics of the data. In complex settings, designing and selecting suitable summary statistics is problematic and results are very sensitive to such choices. In this paper, we learn the first exchangeable feature representation for population genetic data to work directly with genotype data. This is achieved by means of a novel Bayesian likelihood-free inference framework, where a permutation-invariant convolutional neural network learns the inverse functional relationship from the data to the posterior. We leverage access to scientific simulators to learn such likelihood-free function mappings, and establish a general framework for inference in a variety of simulation-based tasks. We demonstrate the power of our method on the recombination hotspot testing problem, outperforming the state-of-the-art.